Book ChapterDOI
Image Denoising Method Based on Curvelet Transform in Telemedicine.
Yang Yu,Dan Li,Likai Wang,Weiwei Liu,Kailiang Zhang,Yuan An +5 more
- pp 679-690
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TLDR
Simulation experiments confirm that the new method of image denoising reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.Abstract:
To resolve the problems that the traditional image denoising methods are easy to lose details such as edges and textures, a new method of image denoising was proposed. It based on the Curvelet denoising algorithm, using polynomial interpolation threshold method, combining with Wrapping and Cycle spinning techniques to determine the adaptive threshold of each Curvelet coefficient for denoising the medical images. Simulation experiments confirm that the new method reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.read more
References
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Artifact-Free Wavelet Denoising: Non-convex Sparse Regularization, Convex Optimization
Yin Ding,Ivan Selesnick +1 more
TL;DR: This paper formulates wavelet-TV (WATV) denoising as a unified problem and uses non-convex penalty functions to strongly induce wavelet sparsity.
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TL;DR: To study and reduce the effect of Gibbs ringing artifact on computed diffusion parameters, a discrete-time model is constructed using LaSalle's inequality.
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TL;DR: A fast discrete Curvelet transform (FDCT)-based technique for multi-focus image fusion to address two problems: texture selection in FDCT domain and block effect in spatial-based fusion.