A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image f...A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.展开更多
In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, whi...In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.展开更多
Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus ...Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.展开更多
Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to ...Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.展开更多
Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed...Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.展开更多
The three-dimensional(3D)model is of great significance to analyze the performance of nonwovens.However,the existing modelling methods could not reconstruct the 3D structure of nonwovens at low cost.A new method based...The three-dimensional(3D)model is of great significance to analyze the performance of nonwovens.However,the existing modelling methods could not reconstruct the 3D structure of nonwovens at low cost.A new method based on deep learning was proposed to reconstruct 3D models of nonwovens from multi-focus images.A convolutional neural network was trained to extract clear fibers from sequence images.Image processing algorithms were used to obtain the radius,the central axis,and depth information of fibers from the extraction results.Based on this information,3D models were built in 3D space.Furthermore,self-developed algorithms optimized the central axis and depth of fibers,which made fibers more realistic and continuous.The method with lower cost could reconstruct 3D models of nonwovens conveniently.展开更多
The aim of the paper is to solve the problem of over-segmentation problem generated by Watershed segmentation algorithm or unstable clarity judgment by small areas in image fusion. A multi-focus image fusion algorithm...The aim of the paper is to solve the problem of over-segmentation problem generated by Watershed segmentation algorithm or unstable clarity judgment by small areas in image fusion. A multi-focus image fusion algorithm is proposed based on CNN segmentation and algebraic multi-grid method (CNN-AMG). Firstly, the CNN segmentation result was utilized to instruct the merging process of the regions generated by the Watershed segmentation method. Then the clear regions were selected into the temporary fusion image and the final fusion process was performed according to the clarity evaluation index, which was computed with the algebraic multi-grid method (AMG). The experimental results show that the fused image quality obtained by the CNNAMG algorithm outperforms the traditional fusion methods such as DSIFT fusion method, CNN fusion method, ASR fusion method, GFF fusion method and so on with some evaluation indexes.展开更多
We propose a multi-focus image fusion method,in which a fully convolutional network for focus detection(FD-FCN)is constructed.To obtain more precise focus detection maps,we propose to add skip layers in the network to...We propose a multi-focus image fusion method,in which a fully convolutional network for focus detection(FD-FCN)is constructed.To obtain more precise focus detection maps,we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps.A new training dataset for the proposed network is constructed based on dataset CIFAR-10.The image fusion algorithm using FD-FCN contains three steps:focus maps are obtained using FD-FCN,decision map generation occurs by applying a morphological process on the focus maps,and image fusion occurs using a decision map.We carry out several sets of experiments,and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.展开更多
Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order de...Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets.The original image is decomposed into a base layer and a detail layer.Furthermore,a new fractional-order spatial frequency is built to reflect the clarity of the image.The fractional-order spatial frequency is used as a rule for detail layers fusion,and intuitionistic fuzzy sets are introduced to fuse base layers.Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus image fusion.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
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 presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is i...The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.展开更多
Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image qual...Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method...Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.展开更多
Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive te...Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).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
Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale who...Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more finely.Methods:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to NAC.Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention mechanism.Results:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing sets.For the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing set.In the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,respectively.DLMM significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the model.Furthermore,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.Conclusions:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.展开更多
基金supported by the National Natural Science Foundation of China(No.61174193)
文摘A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.
基金Sponsored by the Nation Nature Science Foundation of China(Grant No.61275010,61201237)the Fundamental Research Funds for the Central Universities(Grant No.HEUCFZ1129,No.HEUCF120805)
文摘In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.
文摘Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.
基金supported by the National Natural Science Foundation of China(6157206361401308)+6 种基金the Fundamental Research Funds for the Central Universities(2016YJS039)the Natural Science Foundation of Hebei Province(F2016201142F2016201187)the Natural Social Foundation of Hebei Province(HB15TQ015)the Science Research Project of Hebei Province(QN2016085ZC2016040)the Natural Science Foundation of Hebei University(2014-303)
文摘Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.
文摘Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.
基金National Natural Science Foundation of China(No.61771123)。
文摘The three-dimensional(3D)model is of great significance to analyze the performance of nonwovens.However,the existing modelling methods could not reconstruct the 3D structure of nonwovens at low cost.A new method based on deep learning was proposed to reconstruct 3D models of nonwovens from multi-focus images.A convolutional neural network was trained to extract clear fibers from sequence images.Image processing algorithms were used to obtain the radius,the central axis,and depth information of fibers from the extraction results.Based on this information,3D models were built in 3D space.Furthermore,self-developed algorithms optimized the central axis and depth of fibers,which made fibers more realistic and continuous.The method with lower cost could reconstruct 3D models of nonwovens conveniently.
文摘The aim of the paper is to solve the problem of over-segmentation problem generated by Watershed segmentation algorithm or unstable clarity judgment by small areas in image fusion. A multi-focus image fusion algorithm is proposed based on CNN segmentation and algebraic multi-grid method (CNN-AMG). Firstly, the CNN segmentation result was utilized to instruct the merging process of the regions generated by the Watershed segmentation method. Then the clear regions were selected into the temporary fusion image and the final fusion process was performed according to the clarity evaluation index, which was computed with the algebraic multi-grid method (AMG). The experimental results show that the fused image quality obtained by the CNNAMG algorithm outperforms the traditional fusion methods such as DSIFT fusion method, CNN fusion method, ASR fusion method, GFF fusion method and so on with some evaluation indexes.
基金Project supported by the National Natural Science Foundation of China(No.61801190)the Natural Science Foundation of Jilin Province,China(No.20180101055JC)the Outstanding Young Talent Foundation of Jilin Province,China(No.20180520029JH)。
文摘We propose a multi-focus image fusion method,in which a fully convolutional network for focus detection(FD-FCN)is constructed.To obtain more precise focus detection maps,we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps.A new training dataset for the proposed network is constructed based on dataset CIFAR-10.The image fusion algorithm using FD-FCN contains three steps:focus maps are obtained using FD-FCN,decision map generation occurs by applying a morphological process on the focus maps,and image fusion occurs using a decision map.We carry out several sets of experiments,and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.
文摘Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets.The original image is decomposed into a base layer and a detail layer.Furthermore,a new fractional-order spatial frequency is built to reflect the clarity of the image.The fractional-order spatial frequency is used as a rule for detail layers fusion,and intuitionistic fuzzy sets are introduced to fuse base layers.Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus image fusion.
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 National Natural Science Foundation of China(Grant Nos.82272955 and 22203057)the Natural Science Foundation of Fujian Province(Grant No.2021J011361).
文摘The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.
基金supported by the National Natural Science Foundation of China(NSFC)12333010the National Key R&D Program of China 2022YFF0503002+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)the NSFC 11921003supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
基金supported by the National Key R&D Program of China 2022YFF0503002the National Natural Science Foundation of China(NSFC,Grant Nos.12333010 and 12233012)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.
文摘Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).
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National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金supported by the National Natural Science Foundation of China(No.82371933)the National Natural Science Foundation of Shandong Province of China(No.ZR2021MH120)+1 种基金the Taishan Scholars Project(No.tsqn202211378)the Shandong Provincial Natural Science Foundation for Excellent Young Scholars(No.ZR2024YQ075).
文摘Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more finely.Methods:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to NAC.Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention mechanism.Results:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing sets.For the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing set.In the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,respectively.DLMM significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the model.Furthermore,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.Conclusions:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.
