Title :
An efficient learned dictionary and its application to non-local denoising
Author :
Li, Shutao ; Fang, Leyuan
Author_Institution :
Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Abstract :
This paper proposes a new learned dictionary for sparse representation of given data and suggests a way to apply it to non-local denoising. First, a quad-tree structure is efficiently embedded into a sparse dictionary model. This enables the dictionary to discover the complex structures in the given data and to be easily employed to high dimensional data. Besides, we propose a joint 3-D operation to exploit the correlations among the similar blocks, as the non-local denoising model assumes that there exist mutually similar blocks in nature images. This 3-D operation is achieved by a simple concatenation of the similar patches to a single vector and training the proposed dictionary on it. The experimental results indicate that our approach is competitive with several well known denoising techniques in terms of both PSNR and visual quality.
Keywords :
image denoising; quadtrees; 3D operation; PSNR; learned dictionary; nonlocal denoising; quad-tree structure; sparse dictionary model; sparse representation; visual quality; Approximation algorithms; Dictionaries; Image denoising; Joints; Matching pursuit algorithms; Noise reduction; Training; Dictionary learning; K-SVD; Non-local denoising; Sparse representation;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5652718