A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing m...A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.展开更多
In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relationa...In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.展开更多
Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy min...Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.展开更多
Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the thr...Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.展开更多
Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feat...Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%.展开更多
Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence t...Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence to model irregular objects efficiently in 3D application.In 3D scene,this method can get better visual effect by tracking the viewer's real-time perspective position and projecting the photos from different perspectives dynamically.The philosophy of design,the steps of development and some other relevant topics are discussed in details,and the validity of the algorithm is analyzed.The results demonstrate that this method represents more superiority on simulating irregular objects by applying it to the modeling of virtual museum.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-d...A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by ...Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by Hu et al,which evaluated the survival outcomes of patients with metastatic CRC who received image-guided thermal ablation(IGTA).These findings provide valuable clinical evidence supporting IGTA as a feasible,minimally invasive approach and underscore the prognostic significance of metastatic distribution.However,the study by Hu et al has several limitations,including that not all pulmonary lesions were pathologically confirmed,postoperative follow-up mainly relied on dynamic contrast-enhanced computed tomography,no comparative analysis was performed with other local treatments,and the impact of other imaging features on efficacy and prognosis was not evaluated.Future studies should include complete pathological confirmation,integrate functional imaging and radiomics,and use prospective multicenter collaboration to optimize patient selection standards for IGTA treatment,strengthen its clinical evidence base,and ultimately promote individualized decision-making for patients with metastatic CRC.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a c...Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).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
基金Project supported by the National Natural Science Foundation of China (No 60802013)the Natural Science Foundation of Zhe-jiang Province, China (No Y106574)
文摘A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.
文摘In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.
基金This work was supported by the National Key Research and Development Program of China[grant number 2017YFC0803802]and the National Natural Science Foundation of China[grant number 41771486].
文摘Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models,this paper presents a geometric-constrained multi-view image matching method,based on an energy minimization framework.By employing a geometrical constraint,the cost value of the energy function was calculated from multiple images,and the cost value was aggregated in an image space using a semi-global optimization approach.A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculation cost values.It is based on the known interior orientation parameters,exterior orientation parameters,and a given elevation value.For an efficient and reliable processing of multiple remote sensing images,the proposed matching method was performed via a coarse-to-fine strategy through image pyramid.Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method.Results reveal that the multi-view image matching can improve matching reliability.Moreover,the experimental results show that the proposed method performs better than traditional methods.
基金Supported by the Key R&D Projects in Shaanxi Province(2022JBGS3-08)。
文摘Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.
基金This work is funded by Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant 22A460022Training Plan for Young Backbone Teachers in Colleges and Universities in Henan Province under Grant 2021GGJS077.
文摘Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%.
文摘Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence to model irregular objects efficiently in 3D application.In 3D scene,this method can get better visual effect by tracking the viewer's real-time perspective position and projecting the photos from different perspectives dynamically.The philosophy of design,the steps of development and some other relevant topics are discussed in details,and the validity of the algorithm is analyzed.The results demonstrate that this method represents more superiority on simulating irregular objects by applying it to the modeling of virtual museum.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金funded by the National Natural Science Foundation of China(NNSFC)under Grant Numbers 42322408,42188101,and 42441809Additional support was provided by the Climbing Program of the National Space Science Center(NSSC,Grant No.E4PD3005)as well as the Specialized Research Fund for State Key Laboratories of China.
文摘A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
文摘Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by Hu et al,which evaluated the survival outcomes of patients with metastatic CRC who received image-guided thermal ablation(IGTA).These findings provide valuable clinical evidence supporting IGTA as a feasible,minimally invasive approach and underscore the prognostic significance of metastatic distribution.However,the study by Hu et al has several limitations,including that not all pulmonary lesions were pathologically confirmed,postoperative follow-up mainly relied on dynamic contrast-enhanced computed tomography,no comparative analysis was performed with other local treatments,and the impact of other imaging features on efficacy and prognosis was not evaluated.Future studies should include complete pathological confirmation,integrate functional imaging and radiomics,and use prospective multicenter collaboration to optimize patient selection standards for IGTA treatment,strengthen its clinical evidence base,and ultimately promote individualized decision-making for patients with metastatic CRC.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金funded by the National Natural Science Foundation of China(Grant No.6240072655)the Hubei Provincial Key Research and Development Program(Grant No.2023BCB151)+1 种基金the Wuhan Natural Science Foundation Exploration Program(Chenguang Program,Grant No.2024040801020202)the Natural Science Foundation of Hubei Province of China(Grant No.2025AFB148).
文摘Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).
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.
