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Bai Zhi-can

Bio: Bai Zhi-can is an academic researcher. The author has contributed to research in topics: Curvelet & Wavelet packet decomposition. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal Article
TL;DR: Compared to the wavelet transform the curvelet transform is more superior in expressing curve singular func-tion anisotropy and image edge and a new denoising method combining with cycle shift,Ell iterative and Monte Carlo methods’ threshold rule is proposed.
Abstract: Compared to the wavelet transform the curvelet transform is more superior in expressing curve singular func-tion anisotropy and image edge.In view of the insufficiency of wavelet image noise reduction,the characteristics of the fast discrete curvelet transform based on the wrapping algorithm is analyzed,and a new denoising method combining with cycle shift,Ell iterative and Monte Carlo methods’ threshold rule is proposed.This algorithm fully uses the corre-lation of curvelet coefficient to eliminate the scratch distortion and the ringing effect caused by lack of image translation invariability of the Curvelet transform.Experimental results show that the algorithm yields denoised images with higher PSNR and better visual effects in comparison with traditional wavelet denoising,second-generation wavelet denoising,wavelet packet denoising and hard threshold denosing based on curvelet transform.

1 citations


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Proceedings ArticleDOI
11 Dec 2020
TL;DR: Wang et al. as discussed by the authors proposed a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm, which can effectively suppress noise, enhance the edges and details of the image, and has better visual effects.
Abstract: This paper proposes a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm. First, the MRI image is subjected to curvelet positive transform to obtain the curvelet coefficients at various scales and directions, and then the Monte-Carlo test method is used to estimate each scale noise variance, and then apply hard threshold shrinkage processing to the curvelet coefficients. Finally, the Pal-King algorithm with modified membership function is used to perform fuzzy enhancement on the image after inverse curvelet transformation to obtain the final result image. We selected a brain MRI image to test the algorithm, the experimental results show that compared with the other two enhancement algorithms, the algorithm in this paper has higher PSNR and CONTRAST, which can effectively suppress noise, enhance the edges and details of the image, and has better visual effects.