Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image sequence denoising via sparse and redundant representations
IEEE Transactions on Image Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
Sparsity-based image denoising via dictionary learning and structural clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
The SURE-LET Approach to Image Denoising
IEEE Transactions on Image Processing
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
IEEE Transactions on Image Processing
Online dictionary learning algorithm with periodic updates and its application to image denoising
Expert Systems with Applications: An International Journal
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Redundant dictionary learning based image noise reduction methods explore the sparse prior of patches and have proved to lead to state-of-the-art results; however, they do not explore the non-local similarity of image patches. In this paper we exploit both the structural similarities and sparse prior of image patches and propose a new dictionary learning and similarity regularization based image noise reduction method. By formulating the image noise reduction as a multiple variables optimization problem, we alternately optimize the variables to obtain the denoised image. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual results and some numerical guidelines.