From sparse representation to low rank representation (ii)

From sparse representation to low rank representation (ii)

Determine the direction of research has been in the mad theory, recently read some articles, have some ideas, by the way also summed up the representation series of articles, because I just contact, may be some shortcomings, I would like you to correct.

The series of articles from sparse representations to low-rank representations include the following:

First, sparse representation

Ii. NCSR (nonlocallycentralized Sparse representation)

SAN,GHP (gradienthistogram preservation)

Iv. Group sparsity

Wu, Rankdecomposition

Second, nonlocallycentralized Sparse representation

This section is a sequel to the previous article, introducing the improvement of sparse representation

Related method be supposed: NCSR (ICCV ' one, TIP ')

a simple and very effective sparserepresentation model was proposed. It outperforms many state-of-the-arts inimage denoising, deblurring and super-resolution.

Related paper:

[1] W. Dong, L. Zhang and G. Shi, "Centralized Sparse representation for imagerestoration", ICCV 2011.

[2] W. Dong, L. Zhang, G. Shi and X.li, "nonlocallycentralized Sparse Representation for Image Restoration",IEEE Trans. Imageprocessing,vol, no. 4, pp.1620-1630, April.

Ncsr:the idea

Ncsr:the objective function

Ncsr:the Solution

Nscr:the Parameters Anddictionaries

Denoising Results

Deblurring Results

Not finished, to be continued, more please pay attention to Http://blog.csdn.net/tiandijun, Welcome to Exchange!

From sparse representation to low rank representation (ii)