The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification acc...The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification accuracy is investigated by converting red green blue （RGB） color space to various other color spaces. The results show that the classification performance generally changes to a large degree when performing color texture classification in various color spaces, and the opponent RGB-based wavelet scattering network outperforms other color spaces-based wavelet scattering networks. Considering that color spaces can be changed into each other, therefore, when dealing with the problem of color texture classification, converting other color spaces to the opponent RGB color space is recommended before performing the wavelet scattering network.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the...Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the basic elements in successfully utilizing a color mechanism scheme and objectively evaluating fabric color alterations. Precise color measurement, however, is mostly used in sample analysis and quality inspection which help to produce reproducible or similar product. It is important that for quality inspection, the color of the product should be measured as a necessary requirement of quality control whether the product is to be accepted or not. Presented in this study is an unsupervised segmentation of printed fabrics patterns using mean shift algorithm and color measurements over the segmented regions of printed fabric patterns. The results established a consistent and reliable color measurement of multiple color patterns and appearance with the established range without any interactions.展开更多
In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or...In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.展开更多
A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between th...A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.展开更多
This paper develops a wide-band multi-spectral space for color representationwith Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matchingmethod aims at improving the accuracy of color...This paper develops a wide-band multi-spectral space for color representationwith Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matchingmethod aims at improving the accuracy of color representation as well as reducing costs forprocessing and storing multi-spectral images. The results show that the space can present ourexperimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with highefficiency, and that the spanning space with three eigenvectors can present the original space atmore than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole o-riginal spaces.展开更多
In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using onl...In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.展开更多
Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and s...Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and sorting of some single-color fruits such as banana and plum. Fruit images were captured using a color digital camera with capturing direction of zero degree and under illuminant D65. It was observed that growing decay and time-aging made surface color changes in bruised parts of the object. 3D RGB and HSV color vectors as well as a single channel like H （hue）, S （saturation）, V （value） and grey scale images were applied for color quantization of the object. Results showed that there was a distinct threshold in the histogram of the S channel of images which can be applied to separate the object from its background. Moreover, the color change via the defect and time-aging is correctly distinguishable in the hue channel image. The effect of illumination, gloss and shadow of 3D image processing is less noticeable for hue data in comparison to saturation and value. The value of H channel was quantized to five groups based on the difference between each pixel value and the H value of a healthy object. The percentage of different degree of defects can be computed and used for grading the fruits.展开更多
A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is...A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.展开更多
Conventional multispectral space for color representation is modified by algorithm of cluster analysis and principal component analysis. Results show that the newer modified multispectral space not only keeping the st...Conventional multispectral space for color representation is modified by algorithm of cluster analysis and principal component analysis. Results show that the newer modified multispectral space not only keeping the structure of the original space but eliminating zero crosses as well its accuracy of color representation is higher than the conventional.展开更多
Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem ...Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem of building energy consumption has received growing attention.This paper explores the impact of energy-saving decorations in flexible interior space on energy-saving effect of buildings so as to broaden the horizon of energy conservation in building,thereby alleviating the problem of energy shortage in China.展开更多
With the acceleration of China’s aging process and the rapid development of social economy,the government’s strong support for the integration of medical and nursing has made the construction of medical and nursing ...With the acceleration of China’s aging process and the rapid development of social economy,the government’s strong support for the integration of medical and nursing has made the construction of medical and nursing buildings more efficient.At the same time,the need for old-age care is more diversified and hierarchical,and the life cycle is more obvious.Designing an aging color environment for the elderly with different visual abilities in medical care buildings has become a problem for investors,operators and designers.This paper takes the indoor color of medical and nursing buildings as the research object,and combines the characteristics of the elderly’s visual acuity and the ratio and efficacy of indoor colors to investigate two high-end medical and nursing institutions in Beijing and Nanjing.Based on the physical,psychological and behavioral needs,this paper explores the appropriate aging color design method for the living space of the elderly in the medical institutions,aiming to make a meagre effort for the in-depth research and practice of indoor environment design of medical institutions in the future.展开更多
Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics ...Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.展开更多
Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteri...Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteristics in traditional Chinese medicine(TCM)inspection,thereby addressing the critical challenge of privacy preservation in medical image analysis.Methods A facial image dataset was constructed from participants at Nanjing University of Chinese Medicine between April 23 and June 10,2023,using a TCM full-body inspection data acquisition equipment under controlled illumination.The proposed FCP-GAN model was designed to achieve the dual objectives of removing identity features and preserving colors through three key components:(i)a multi-space combination module that comprehensively extracts color attributes from red,green,blue(RGB),hue,saturation,value(HSV),and Lab spaces;(ii)a generator incorporating efficient channel attention(ECA)mechanism to enhance the representation of diagnostically critical color channels;and(iii)a dual-loss function that combines adversarial loss for de-identification with a dedicated color preservation loss.The model was trained and evaluated using a stratified 5-fold cross-validation strategy and evaluated against four baseline generative models:conditional GAN(CGAN),deep convolutional GAN(DCGAN),dual discriminator CGAN(DDCGAN),and medical GAN(MedGAN).Performance was assessed in terms of image quality[peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)],distribution similarity[Fréchet inception distance(FID)],privacy protection(face recognition accuracy),and diagnostic consistency[mean squared error(MSE)and Pearson correlation coefficient(PCC)].Results The final analysis included facial images from 216 participants.Compared with baseline models,FCP-GAN achieved superior performance,with PSNR=31.02 dB and SSIM=0.908,representing an improvement of 1.21 dB and 0.034 in SSIM over the strongest baseline(MedGAN).The FID value(23.45)was also the lowest among all models,indicating superior distributional similarity to real images.The multi-space feature fusion and the ECA mechanism contributed significantly to these performance gains,as evidenced by ablation studies.The stratified 5-fold cross-validation confirmed the model’s robustness,with results reported as mean±standard deviation(SD)across all folds.The model effectively protected privacy by reducing face recognition accuracy from 95.2%(original images)to 60.1%(generated images).Critically,it maintained high diagnostic fidelity,as evidenced by a low MSE(<0.051)and a high PCC(>0.98)for key TCM facial features between original and generated images.Conclusion The FCP-GAN model provides an effective technical solution for ensuring privacy in TCM diagnostic imaging,successfully having removed identity features while preserving clinically vital facial color features.This study offers significant value for developing intelligent and secure TCM telemedicine systems.展开更多
基金The National Basic Research Program of China(No.2011CB707904)the National Natural Science Foundation of China(No.61201344,61271312,11301074)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK2012329)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092110023,20120092120036)
文摘The optimized color space is searched by using the wavelet scattering network in the KTH_TIPS_COL color image database for image texture classification. The effect of choosing the color space on the classification accuracy is investigated by converting red green blue （RGB） color space to various other color spaces. The results show that the classification performance generally changes to a large degree when performing color texture classification in various color spaces, and the opponent RGB-based wavelet scattering network outperforms other color spaces-based wavelet scattering networks. Considering that color spaces can be changed into each other, therefore, when dealing with the problem of color texture classification, converting other color spaces to the opponent RGB color space is recommended before performing the wavelet scattering network.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
文摘Colors of textile materials are the first parameter of quality evaluated by consumers and a key component considered in selecting printed fabric. In the textiles industry, digital printed fabric analysis is one of the basic elements in successfully utilizing a color mechanism scheme and objectively evaluating fabric color alterations. Precise color measurement, however, is mostly used in sample analysis and quality inspection which help to produce reproducible or similar product. It is important that for quality inspection, the color of the product should be measured as a necessary requirement of quality control whether the product is to be accepted or not. Presented in this study is an unsupervised segmentation of printed fabrics patterns using mean shift algorithm and color measurements over the segmented regions of printed fabric patterns. The results established a consistent and reliable color measurement of multiple color patterns and appearance with the established range without any interactions.
基金This research was supported by The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]the Science and Technology Department of Jilin Province[20160623016TC,20170204017NY,20170204038NY(Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn],and the ScienceTechnology Bureau of Changchun City[18DY021(Mu Y.,Hu T.L.,Gong H.,and Sun Y.H.)http://kjj.changchun.gov.cn].
文摘In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.
基金the National Natural Science Foundation of China (No. 60675023)
文摘A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.
基金rojectsupportedbytheNationalNaturalScienceFoundationofChina (No .60 1 770 0 5) .
文摘This paper develops a wide-band multi-spectral space for color representationwith Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matchingmethod aims at improving the accuracy of color representation as well as reducing costs forprocessing and storing multi-spectral images. The results show that the space can present ourexperimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with highefficiency, and that the spanning space with three eigenvectors can present the original space atmore than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole o-riginal spaces.
文摘In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.
文摘Machine vision has been recently utilized for quality control of food and agricultural products, which was traditionally done by manual inspection. The present study was an attempt for automatic defect detection and sorting of some single-color fruits such as banana and plum. Fruit images were captured using a color digital camera with capturing direction of zero degree and under illuminant D65. It was observed that growing decay and time-aging made surface color changes in bruised parts of the object. 3D RGB and HSV color vectors as well as a single channel like H （hue）, S （saturation）, V （value） and grey scale images were applied for color quantization of the object. Results showed that there was a distinct threshold in the histogram of the S channel of images which can be applied to separate the object from its background. Moreover, the color change via the defect and time-aging is correctly distinguishable in the hue channel image. The effect of illumination, gloss and shadow of 3D image processing is less noticeable for hue data in comparison to saturation and value. The value of H channel was quantized to five groups based on the difference between each pixel value and the H value of a healthy object. The percentage of different degree of defects can be computed and used for grading the fruits.
文摘A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.
文摘Conventional multispectral space for color representation is modified by algorithm of cluster analysis and principal component analysis. Results show that the newer modified multispectral space not only keeping the structure of the original space but eliminating zero crosses as well its accuracy of color representation is higher than the conventional.
基金Sponsored by Education Science Project of the 13th Five-Year Plan of Jiangxi Province(16YB041)
文摘Green,energy conservation and environmental protection have increasingly become the theme of the sustained and healthy development of cities against the background of new urbanization,which indicates that the problem of building energy consumption has received growing attention.This paper explores the impact of energy-saving decorations in flexible interior space on energy-saving effect of buildings so as to broaden the horizon of energy conservation in building,thereby alleviating the problem of energy shortage in China.
基金Social Science Planning Fund Project in Liaoning Province in 2021(L21BRK003).
文摘With the acceleration of China’s aging process and the rapid development of social economy,the government’s strong support for the integration of medical and nursing has made the construction of medical and nursing buildings more efficient.At the same time,the need for old-age care is more diversified and hierarchical,and the life cycle is more obvious.Designing an aging color environment for the elderly with different visual abilities in medical care buildings has become a problem for investors,operators and designers.This paper takes the indoor color of medical and nursing buildings as the research object,and combines the characteristics of the elderly’s visual acuity and the ratio and efficacy of indoor colors to investigate two high-end medical and nursing institutions in Beijing and Nanjing.Based on the physical,psychological and behavioral needs,this paper explores the appropriate aging color design method for the living space of the elderly in the medical institutions,aiming to make a meagre effort for the in-depth research and practice of indoor environment design of medical institutions in the future.
基金Supported by the National Key R&D Program of China (No:2018YFB1403202)the National Natural Science Foundation of China(62172366)。
文摘Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.
基金National Key Research and Development Program of China(2022YFC3502302)Graduate Research Innovation Program of Jiangsu Province(KYCX25_2269)。
文摘Objective To develop a facial image generation method based on a facial color-preserving generative adversarial network(FCP-GAN)that effectively decouples identity features from diagnostic facial complexion characteristics in traditional Chinese medicine(TCM)inspection,thereby addressing the critical challenge of privacy preservation in medical image analysis.Methods A facial image dataset was constructed from participants at Nanjing University of Chinese Medicine between April 23 and June 10,2023,using a TCM full-body inspection data acquisition equipment under controlled illumination.The proposed FCP-GAN model was designed to achieve the dual objectives of removing identity features and preserving colors through three key components:(i)a multi-space combination module that comprehensively extracts color attributes from red,green,blue(RGB),hue,saturation,value(HSV),and Lab spaces;(ii)a generator incorporating efficient channel attention(ECA)mechanism to enhance the representation of diagnostically critical color channels;and(iii)a dual-loss function that combines adversarial loss for de-identification with a dedicated color preservation loss.The model was trained and evaluated using a stratified 5-fold cross-validation strategy and evaluated against four baseline generative models:conditional GAN(CGAN),deep convolutional GAN(DCGAN),dual discriminator CGAN(DDCGAN),and medical GAN(MedGAN).Performance was assessed in terms of image quality[peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)],distribution similarity[Fréchet inception distance(FID)],privacy protection(face recognition accuracy),and diagnostic consistency[mean squared error(MSE)and Pearson correlation coefficient(PCC)].Results The final analysis included facial images from 216 participants.Compared with baseline models,FCP-GAN achieved superior performance,with PSNR=31.02 dB and SSIM=0.908,representing an improvement of 1.21 dB and 0.034 in SSIM over the strongest baseline(MedGAN).The FID value(23.45)was also the lowest among all models,indicating superior distributional similarity to real images.The multi-space feature fusion and the ECA mechanism contributed significantly to these performance gains,as evidenced by ablation studies.The stratified 5-fold cross-validation confirmed the model’s robustness,with results reported as mean±standard deviation(SD)across all folds.The model effectively protected privacy by reducing face recognition accuracy from 95.2%(original images)to 60.1%(generated images).Critically,it maintained high diagnostic fidelity,as evidenced by a low MSE(<0.051)and a high PCC(>0.98)for key TCM facial features between original and generated images.Conclusion The FCP-GAN model provides an effective technical solution for ensuring privacy in TCM diagnostic imaging,successfully having removed identity features while preserving clinically vital facial color features.This study offers significant value for developing intelligent and secure TCM telemedicine systems.
