Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Variational Approach to Reconstructing Images Corrupted by Poisson Noise
Journal of Mathematical Imaging and Vision
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Journal of Mathematical Imaging and Vision
Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
SIAM Journal on Imaging Sciences
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Journal of Mathematical Imaging and Vision
Multiplicative Noise Removal with Spatially Varying Regularization Parameters
SIAM Journal on Imaging Sciences
Nonlocal filters for removing multiplicative noise
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Adaptive Fractional-order Multi-scale Method for Image Denoising
Journal of Mathematical Imaging and Vision
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Variational denoising of partly textured images by spatially varying constraints
IEEE Transactions on Image Processing
Fractional-Order Anisotropic Diffusion for Image Denoising
IEEE Transactions on Image Processing
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In this paper, we introduce a simple reweighted residual-feedback iterative (RRFI) algorithm which provides a general framework to solve the fractional-order total variation regularized models with different fidelity terms. We provide a sufficient condition for the convergence of this algorithm. As an application, we use this algorithm to solve the TV and fractional-order TV regularized models with two special fidelity terms for multiplicative noise removal of partly-textured images. To improve the performance, we define gradually varying fuzzy membership degrees to mark the possibilities of a pixel belonging to edges, textured regions and flat regions. Using the fuzzy membership degrees, we add local behavior to the choice of the parameters and the updating of the weighting matrix, and then propose an adaptive RRFI algorithm for multiplicative noise removal. Numerical results show that the RRFI algorithm has low computational cost and fast convergence speed. The adaptive RRFI algorithm performs well for preserving details and eliminating the staircase effect while removing noise, and therefore can improve the result visually efficiently.