Ten lectures on wavelets
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
Variational methods in image segmentation
Variational methods in image segmentation
A Variational Method in Image Recovery
SIAM Journal on Numerical Analysis
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
SIAM Journal on Numerical Analysis
Inverse Scale Spaces for Nonlinear Regularization
Journal of Mathematical Imaging and Vision
Nonlinear inverse scale space methods for image restoration
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space
IEEE Transactions on Image Processing
Iterative Regularization and Nonlinear Inverse Scale Space Applied to Wavelet-Based Denoising
IEEE Transactions on Image Processing
Hi-index | 0.00 |
We first generalize the wavelet-based iterative regularization method and the wavelet-based inverse scale space to shift invariant wavelet-based cases for image restoration. Then, a method to estimate the scale parameter is proposed from wavelet-based iterative regularization; different parameters with different iterations are obtained. The wavelet-based iterative regularization with the new parameter, which controls the extent of denoising more precisely in the wavelet domain, leads to iterative global wavelet shrinkage. We also obtain a time adaptive wavelet-based inverse scale space from the iterative procedure with the proposed parameter. We provide a proof of the convergence and obtain a stopping criterion for the iterative procedure with the new scale parameter based on wavelet transform. The proposed iterative regularized method obtains quite accurate results on a variety of images. Numerical experiments show that the proposed methods can efficiently remove noise and well preserve the details of images.