Denoising natural images based on a modified sparse coding algorithm

• Published in: Applied mathematics and computation Vol. 205; no. 2; pp. 883 - 889
• Main Author: Shang, Li
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier Inc 15.11.2008; Elsevier
• Subjects: Algebra; Exact sciences and technology; Fixed variance; Global analysis, analysis on manifolds; Image feature extraction; Image reconstruction; Kurtosis; Linear and multilinear algebra, matrix theory; Mathematical analysis; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Sciences and techniques of general use; Sparse coding; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds; Fixed variance; Kurtosis; Sparse coding; Image feature extraction; Image reconstruction; Algorithm; Variance; Convergence speed; Denoising; Experimental result; Numerical analysis; Fix point; Coding; Applied mathematics; Algorithm analysis
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2008.05.018

This paper proposes a novel image reconstruction method for natural images using a modified sparse coding (SC) algorithm proposed by us. This SC algorithm exploited the maximum Kurtosis as the maximizing sparse measure criterion at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. On the other hand, in order to improve the convergence speed, we use a determinative basis function, which is obtained by a fast fixed-point independent component analysis (FastICA) algorithm, as the initialization feature basis function of our sparse coding algorithm instead of using a random initialization matrix. The experimental results show that by using our SC algorithm, the feature basis vectors of natural images can be successfully extracted. Then, exploiting these features, the original images can be reconstructed easily. Furthermore, compared with the standard ICA method, the experimental results show that our SC algorithm is indeed efficient and effective in performing image reconstruction task.