Adaptive image enhancement using nonsubsampled contourlet transform domain histogram matching

2014 ◽  
Vol 12 (s2) ◽  
pp. S21002-321005
Author(s):  
Yan Zhou Yan Zhou ◽  
Qingwu Li Qingwu Li ◽  
Guanying Huo Guanying Huo
Keyword(s):  
Image Enhancement ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform ◽  
Histogram Matching

scholarly journals Infrared and Visible Image Fusion Method Based on ResNet in a Nonsubsampled Contourlet Transform Domain

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ce Gao ◽  
Donghao Qi ◽  
Yanchao Zhang ◽  
Congcong Song ◽  
Yi Yu
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Visible Image ◽  
Image Fusion Method

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

Coefficient difference based watermark detector in nonsubsampled contourlet transform domain

2019 ◽  
Vol 503 ◽  
pp. 274-290 ◽  
Author(s):  
Xiang-yang Wang ◽  
Si-yu Zhang ◽  
Tao-tao Wen ◽  
Hong-ying Yang ◽  
Pan-pan Niu
Keyword(s):  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform

A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2508-2511 ◽  
Author(s):  
Jingjing Wang ◽  
Qian Li ◽  
Zhenhong Jia ◽  
Nikola Kasabov ◽  
Jie Yang
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Focus Image ◽  
Image Fusion Method

Image denoising using SVM classification in nonsubsampled contourlet transform domain

2013 ◽  
Vol 246 ◽  
pp. 155-176 ◽  
Author(s):  
Xiang-Yang Wang ◽  
Hong-Ying Yang ◽  
Yu Zhang ◽  
Zhong-Kai Fu
Keyword(s):  
Image Denoising ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Nonsubsampled Contourlet Transform ◽  
Svm Classification
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