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Low-light color image enhancement based on NSST

The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 41-48页

作者： Wu Xiaochu Tang Guijin Liu Xiaohua Cui Ziguan Luo Suhuai Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Electrical Engineering and Computing the University of NewcastleNSW 2308Australia

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this *** steps of the proposed algorithm are described as ***,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value ***,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency ***,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST ***,the image is converted back to the RGB color space to obtain the enhanced *** results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

关键词： non-subsampled shearlet transform guided image filtering low-light image enhancement the HSV color space

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Skin Lesion Classification System Using shearlets

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Computer Systems Science & Engineering 2023年第1期44卷 833-844页

作者： S.Mohan Kumar T.Kumanan Department of Computer Science and Engineering Meenakshi Academy of Higher Education and ResearchChennai600078Tamil NaduIndia

The main cause of skin cancer is the ultraviolet radiation of the *** spreads quickly to other body ***,early diagnosis is required to decrease the mortality rate due to skin *** this study,an automatic system for Skin Lesion Classification(SLC)using non-subsampled shearlet transform(NSST)based energy features and Support Vector Machine(SVM)classifier is ***,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and *** the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for *** classifier is used for the classification of skin lesion *** dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic *** performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)*** SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

关键词： Skin lesion classification non-subsampled shearlet transform sub-band coefficients energy feature support vector machine

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Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

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Instrumentation 2023年第1期10卷 45-58页

作者： WANG Wenyan ZHOU Xianchun YANG Liangjian Nanjing University of Information Science and Technology Nanjing 210044

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment *** at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is *** method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband ***,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried *** experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

关键词： Image Fusion non-subsampled shearlet transform Parameter Adaptive PCNN Latent Low-rank Representation

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