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Mathematical Problems in Engineering
Volume 2014, Article ID 368602, 10 pages
Research Article

Dictionary-Based Image Denoising by Fused-Lasso Atom Selection

1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
2Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan

Received 7 May 2014; Revised 12 August 2014; Accepted 13 August 2014; Published 28 August 2014

Academic Editor: Carla Roque

Copyright © 2014 Ao Li and Hayaru Shouno. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA) with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.