Blind motion deblurring using multiple images
Journal of Computational Physics
On the total variation dictionary model
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Wavelet-type denoising for mechanical structures diagnosis
EMESEG'10 Proceedings of the 3rd WSEAS international conference on Engineering mechanics, structures, engineering geology
A novel predual dictionary learning algorithm
Journal of Visual Communication and Image Representation
Analysis and Generalizations of the Linearized Bregman Method
SIAM Journal on Imaging Sciences
An augmented Lagrangian approach to general dictionary learning for image denoising
Journal of Visual Communication and Image Representation
Error Forgetting of Bregman Iteration
Journal of Scientific Computing
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In this paper, we generalize the iterative regularization method and the inverse scale space method, recently developed for total-variation (TV) based image restoration, to wavelet-based image restoration. This continues our earlier joint work with others where we applied these techniques to variational-based image restoration, obtaining significant improvement over the Rudin-Osher-Fatemi TV-based restoration. Here, we apply these techniques to soft shrinkage and obtain the somewhat surprising result that a) the iterative procedure applied to soft shrinkage gives firm shrinkage and converges to hard shrinkage and b) that these procedures enhance the noise-removal capability both theoretically, in the sense of generalized Bregman distance, and for some examples, experimentally, in terms of the signal-to-noise ratio, leaving less signal in the residual