In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff...In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).展开更多
Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution...Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution and wide FOV,infrared optical systems often adopt complex optical lens groups,which will increase the size and weight of the optical system.In this paper,a strategy based on wavefront coding(WFC)is proposed to design a compact wide-FOV infrared imager.A cubic phase mask is inserted into the pupil plane of the infrared imager to correct the aberration.The simulated results show that,the WFC infrared imager has good imaging quality in a wide FOV of±16°.In addition,the WFC infrared imager achieves compactness with its 40 mm×40 mm×40 mm size.A fast focal ratio of 1 combined with an entrance pupil diameter of 25 mm ensures brightness.This work is of significance for designing a compact wide-FOV infrared imager.展开更多
Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images.Despite the significant progress of deep learning-based fusion methods,existing...Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images.Despite the significant progress of deep learning-based fusion methods,existing algorithms are often limited to single or dual-dimensional feature interactions,thus struggling to fully exploit the profound complementarity between multi-modal images.To address this,this paper proposes a parallel multidimensional complementary fusion network,termed PMCFusion,for the task of infrared and visible image fusion.The core of this method is its unique parallel three-branch fusion module,PTFM,which pioneers the parallel synergistic perception and efficient integration of three distinct dimensions:spatial uncorrelation,channel-wise disparity,and frequency-domain complementarity.Leveraging meticulously designed cross-dimensional attention interactions,PTFM can selectively enhance multi-dimensional features to achieve deep complementarity.Furthermore,to enhance the detail clarity and structural integrity of the fused image,we have designed a dedicated multi-scale high-frequency detail enhancement module,HFDEM.It effectively improves the clarity of the fused image by actively extracting,enhancing,and injecting high-frequency components in a residual manner.The overall model employs a multi-scale architecture and is constrained by corresponding loss functions to ensure efficient and robust fusion across different resolutions.Extensive experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art fusion algorithms in both subjective visual effects and objective evaluation metrics.展开更多
Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the pri...Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the principles,applications,and future potential of infrared thermal imaging technology in the field.Key applications include measuring soil and rock properties,conducting geotechnical surveys,and monitoring geological hazards.Infrared thermal imaging technology has proven effective in detecting thermal anomalies,assessing geotechnical material characteristics,and monitoring hazards such as landslides and rockfalls.Despite its broad applications,challenges persist,including thermal interference,limitations in data processing,and complexities in technology integration.This review outlines advancements needed in algorithm optimization,integration with complementary technologies,and the expansion of applications into emerging areas such as ecological geotechnical engineering and heritage preservation.Addressing these challenges will unlock the full potential of infrared thermal imaging technology,positioning it as an essential tool for enhancing the safety,efficiency,and sustainability of geotechnical engineering practices.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,exis...The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,existing image fusion algorithms are generally suitable for normal scenes.In the hazy scene,a lot of texture information in the visible image is hidden,the results of existing methods are filled with infrared information,resulting in the lack of texture details and poor visual effect.To address the aforementioned difficulties,we propose a haze-free infrared and visible fusion method,termed HaIVFusion,which can eliminate the influence of haze and obtain richer texture information in the fused image.Specifically,we first design a scene information restoration network(SIRNet)to mine the masked texture information in visible images.Then,a denoising fusion network(DFNet)is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible.In addition,we use color consistency loss to reduce the color distortion resulting from haze.Furthermore,we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes.Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes,and achieves better quantitative results,when compared to state-ofthe-art image fusion methods,even combined with state-of-the-art dehazing methods.展开更多
Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illuminati...Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.展开更多
The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene.However,existing methods struggle to effectively han...The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene.However,existing methods struggle to effectively handle modal disparities,resulting in visual degradation of the details and prominent targets of the fused images.To address these challenges,we introduce Prompt Fusion,a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts.Firstly,to better characterize the features of different modalities,a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities,thereby improving the extraction of fine details and textures.We also introduce a prompt learning mechanism using positive and negative prompts,leveraging Vision-Language Models to improve the fusion model's understanding and identification of targets in multi-modality images,leading to improved performance in downstream tasks.Furthermore,we employ bi-level asymptotic convergence optimization.This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient descent.Our approach advances the state-of-the-art,delivering superior fusion quality and boosting the performance of related downstream tasks.Project page:https://github.com/hey-it-s-me/PromptFusion.展开更多
Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the preva...Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.展开更多
As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial ...As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.展开更多
Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on e...Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.展开更多
Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing method...Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).展开更多
This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. T...This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...展开更多
To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation f...To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.展开更多
In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusi...In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.展开更多
The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information ...The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2 is described. The result shows that this is a high efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.展开更多
Using 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran(TCF)as a near-infrared fluorescent chromophore,we designed and synthesized a TCF-based fluorescent probe TCF-NS by introducing 2,4-dinitrophenyl ether ...Using 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran(TCF)as a near-infrared fluorescent chromophore,we designed and synthesized a TCF-based fluorescent probe TCF-NS by introducing 2,4-dinitrophenyl ether as the recognized site for H_(2)S.The probe TCF-NS displayed a rapid-response fluorescent against H_(2)S with high sensitivity and selection but had no significant fluorescence response to other biothiols.Furthermore,TCF-NS was applied to sense H_(2)S in living cells successfully with minimized cytotoxicity and a large Stokes shift.展开更多
Mid-infrared(MIR)spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints,which calls for high-sensitivity broadband MIR imagers at room temperature.Here,we devise an...Mid-infrared(MIR)spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints,which calls for high-sensitivity broadband MIR imagers at room temperature.Here,we devise and implement a continuous-wave pumping MIR upconversion imaging system based on externalcavity enhancement,which favors a large field of view,a low cavity loss,and a high spectral resolution.The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror,which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm.In combination with the chirped-poling crystal design,high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle of up to 28.5 deg over a spectral coverage of 2.5 to 5μm.Moreover,a thermal locking approach is used to stabilize the cavity at high-power operation,eliminating active feedback and ensuring long-term stability.A proof-of-principle demonstration is presented to showcase real-time observation of CO_(2)gas injection dynamics.The implemented MIR upconversion imager features wide-field operation,high detection sensitivity,and compact footprint,which would benefit subsequent applications,including environment monitoring,gas leakage inspection,and medical diagnostics.展开更多
1.Introduction Infrared Imaging Missiles(IRIMs)are advanced weapons utilizing infrared technology for target detection and tracking.Their sensors capture thermal signatures and convert them into electronic images,enab...1.Introduction Infrared Imaging Missiles(IRIMs)are advanced weapons utilizing infrared technology for target detection and tracking.Their sensors capture thermal signatures and convert them into electronic images,enabling precise target identification and tracking.To a certain extent,the all-weather adaptability of IRIMs enables their effective operation across diverse environmental conditions,providing high targeting accuracy and cost efficiency.展开更多
基金funded by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229)+3 种基金Talent Research Initiation Fund Project of Tongling University(2024tlxyrc019)Tongling University School-Level Scientific Research Project(2024tlxyptZD07)TheUniversity Synergy Innovation Programof Anhui Province(GXXT-2023-050)Tongling City Science and Technology Major Special Project(Unveiling and Commanding Model)(200401JB004).
文摘In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).
文摘Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution and wide FOV,infrared optical systems often adopt complex optical lens groups,which will increase the size and weight of the optical system.In this paper,a strategy based on wavefront coding(WFC)is proposed to design a compact wide-FOV infrared imager.A cubic phase mask is inserted into the pupil plane of the infrared imager to correct the aberration.The simulated results show that,the WFC infrared imager has good imaging quality in a wide FOV of±16°.In addition,the WFC infrared imager achieves compactness with its 40 mm×40 mm×40 mm size.A fast focal ratio of 1 combined with an entrance pupil diameter of 25 mm ensures brightness.This work is of significance for designing a compact wide-FOV infrared imager.
基金supported in part by the Funds for Central-Guided Local Science&Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasmin part by the Xingdian Talent Project of Yunnan Province.The key technology research and application of cross-domain automatic business collaboration in smart tourism(XYYC-CYCX-2022-0005)in part by the Yunnan Province Zhangjun ExpertWorkstation(No.202205AF150081).
文摘Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images.Despite the significant progress of deep learning-based fusion methods,existing algorithms are often limited to single or dual-dimensional feature interactions,thus struggling to fully exploit the profound complementarity between multi-modal images.To address this,this paper proposes a parallel multidimensional complementary fusion network,termed PMCFusion,for the task of infrared and visible image fusion.The core of this method is its unique parallel three-branch fusion module,PTFM,which pioneers the parallel synergistic perception and efficient integration of three distinct dimensions:spatial uncorrelation,channel-wise disparity,and frequency-domain complementarity.Leveraging meticulously designed cross-dimensional attention interactions,PTFM can selectively enhance multi-dimensional features to achieve deep complementarity.Furthermore,to enhance the detail clarity and structural integrity of the fused image,we have designed a dedicated multi-scale high-frequency detail enhancement module,HFDEM.It effectively improves the clarity of the fused image by actively extracting,enhancing,and injecting high-frequency components in a residual manner.The overall model employs a multi-scale architecture and is constrained by corresponding loss functions to ensure efficient and robust fusion across different resolutions.Extensive experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art fusion algorithms in both subjective visual effects and objective evaluation metrics.
基金supported by the National Natural Science Foundation of China(Grant Nos.42461160293 and 42230710)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20221250).
文摘Infrared thermal imaging technology has become a versatile and transformative tool in geotechnical engineering due to its non-contact,high-sensitivity,and real-time monitoring capabilities.This review explores the principles,applications,and future potential of infrared thermal imaging technology in the field.Key applications include measuring soil and rock properties,conducting geotechnical surveys,and monitoring geological hazards.Infrared thermal imaging technology has proven effective in detecting thermal anomalies,assessing geotechnical material characteristics,and monitoring hazards such as landslides and rockfalls.Despite its broad applications,challenges persist,including thermal interference,limitations in data processing,and complexities in technology integration.This review outlines advancements needed in algorithm optimization,integration with complementary technologies,and the expansion of applications into emerging areas such as ecological geotechnical engineering and heritage preservation.Addressing these challenges will unlock the full potential of infrared thermal imaging technology,positioning it as an essential tool for enhancing the safety,efficiency,and sustainability of geotechnical engineering practices.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金supported by the Natural Science Foundation of Shandong Province,China(ZR2022MF237)the National Natural Science Foundation of China Youth Fund(62406155)the Major Innovation Project(2023JBZ02)of Qilu University of Technology(Shandong Academy of Sciences).
文摘The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,existing image fusion algorithms are generally suitable for normal scenes.In the hazy scene,a lot of texture information in the visible image is hidden,the results of existing methods are filled with infrared information,resulting in the lack of texture details and poor visual effect.To address the aforementioned difficulties,we propose a haze-free infrared and visible fusion method,termed HaIVFusion,which can eliminate the influence of haze and obtain richer texture information in the fused image.Specifically,we first design a scene information restoration network(SIRNet)to mine the masked texture information in visible images.Then,a denoising fusion network(DFNet)is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible.In addition,we use color consistency loss to reduce the color distortion resulting from haze.Furthermore,we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes.Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes,and achieves better quantitative results,when compared to state-ofthe-art image fusion methods,even combined with state-of-the-art dehazing methods.
基金This researchwas Sponsored by Xinjiang Uygur Autonomous Region Tianshan Talent Programme Project(2023TCLJ02)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01C349).
文摘Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.
基金partially supported by China Postdoctoral Science Foundation(2023M730741)the National Natural Science Foundation of China(U22B2052,52102432,52202452,62372080,62302078)
文摘The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene.However,existing methods struggle to effectively handle modal disparities,resulting in visual degradation of the details and prominent targets of the fused images.To address these challenges,we introduce Prompt Fusion,a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts.Firstly,to better characterize the features of different modalities,a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities,thereby improving the extraction of fine details and textures.We also introduce a prompt learning mechanism using positive and negative prompts,leveraging Vision-Language Models to improve the fusion model's understanding and identification of targets in multi-modality images,leading to improved performance in downstream tasks.Furthermore,we employ bi-level asymptotic convergence optimization.This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient descent.Our approach advances the state-of-the-art,delivering superior fusion quality and boosting the performance of related downstream tasks.Project page:https://github.com/hey-it-s-me/PromptFusion.
基金funded in part by theHenan ProvinceKeyR&DProgramProject,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”under Grant No.231111210400.
文摘Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.
基金Science and Technology Department of Jilin Province(No.20200403075SF)Education Department of Jilin Province(No.JJKH20240148KJ).
文摘As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.
基金supported by the National Natural Science Foundation of China(62475199,62075169,U23B2050)the Industry University-Research Cooperation Program of Zhuhai(2220004002828).
文摘Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.
基金supported by Universiti Teknologi MARA through UiTM MyRA Research Grant,600-RMC 5/3/GPM(053/2022).
文摘Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).
基金National Natural Science Foundation of China (60802084)
文摘This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...
文摘To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.
基金Open Fund Project of Key Laboratory of Instrumentation Science&Dynamic Measurement(No.2DSYSJ2015005)Specialized Research Fund for the Doctoral Program of Ministry of Education Colleges(No.20121420110004)
文摘In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.
文摘The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2 is described. The result shows that this is a high efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.
基金financially supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20241181)the State Key Laboratory of AnalyticalChemistry for Life Science,School of Chemistry and Chemical Engineering,Nanjing University(Grant No.SKLACLS2419)。
文摘Using 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran(TCF)as a near-infrared fluorescent chromophore,we designed and synthesized a TCF-based fluorescent probe TCF-NS by introducing 2,4-dinitrophenyl ether as the recognized site for H_(2)S.The probe TCF-NS displayed a rapid-response fluorescent against H_(2)S with high sensitivity and selection but had no significant fluorescence response to other biothiols.Furthermore,TCF-NS was applied to sense H_(2)S in living cells successfully with minimized cytotoxicity and a large Stokes shift.
基金supported by the Shanghai Pilot Program for Basic Research (Grant No. TQ20220104)the National Natural Science Foundation of China (Grant Nos. 62175064, 62235019, and 62035005)+4 种基金the Innovation Program for Quantum Science and Technology (Grant No. 2023ZD0301000)the Shanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX01)the Natural Science Foundation of Chongqing (Grant Nos. CSTB2023NSCQ-JQX0011 and CSTB2022TIAD-DEX0036)the Fundamental Research Funds for the Central Universitiesthe China Postdoctoral Science Foundation (Grant No. 2024M760918)
文摘Mid-infrared(MIR)spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints,which calls for high-sensitivity broadband MIR imagers at room temperature.Here,we devise and implement a continuous-wave pumping MIR upconversion imaging system based on externalcavity enhancement,which favors a large field of view,a low cavity loss,and a high spectral resolution.The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror,which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm.In combination with the chirped-poling crystal design,high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle of up to 28.5 deg over a spectral coverage of 2.5 to 5μm.Moreover,a thermal locking approach is used to stabilize the cavity at high-power operation,eliminating active feedback and ensuring long-term stability.A proof-of-principle demonstration is presented to showcase real-time observation of CO_(2)gas injection dynamics.The implemented MIR upconversion imager features wide-field operation,high detection sensitivity,and compact footprint,which would benefit subsequent applications,including environment monitoring,gas leakage inspection,and medical diagnostics.
基金co-supported by the China Postdoctoral Science Foundation(No.2024M754304)the Hunan Provincial Natural Science Foundation of China(No.2025JJ60072)。
文摘1.Introduction Infrared Imaging Missiles(IRIMs)are advanced weapons utilizing infrared technology for target detection and tracking.Their sensors capture thermal signatures and convert them into electronic images,enabling precise target identification and tracking.To a certain extent,the all-weather adaptability of IRIMs enables their effective operation across diverse environmental conditions,providing high targeting accuracy and cost efficiency.
