Analysis Sparse Representation Based on Subset Pursuit and Weighted Split Bregman Iteration Algorithm. (28th October 2015)

• Record Type: Journal Article
• Authors: Zhang, Ye; Yu, Tenglong; Zhang, Wenquan
• Is Part Of: International journal of advanced robotic systems. Volume 12:Number 10(2015)
• Publication Date: 2015-10-28
• Subjects: Sparse representation -- synthesis model -- analysis model -- dictionary learning -- image denoising; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/61543
• Languages: English
• ISSNs: 1729-8806

Abstract

Recently, a sparse representation model - called an analysis sparse model - where the signal is multiplied by an analysis dictionary and the outcome is assumed to be sparse, has received increasing attention since it has potential and extensive applications in the area of signal processing. The performance of the analysis model significantly depends on an appropriately chosen dictionary. Most existing analysis dictionary learning algorithms are based on the assumption that the original signals are known or can be estimated from their noisy versions. Generally, however, the original signals are unknown or need to be estimated by using greedy-like algorithms with heavy computation. To solve the problems, we introduce a subset pursuit algorithm for analysis dictionary learning, where the observed signals are directly employed to learn the analysis dictionary. Next, a weighted split Bregman iteration algorithm is proposed to estimate original signals by the learned analysis dictionary. The experimental results demonstrate the competitive performance of the proposed algorithms compared with the state-of-art algorithms.