Cheng-Wei Sang

Hong Sun, Chen-guang Liu, Cheng-wei Sang

This article addresses the image denoising problem in the situations of strong noise. We propose a dual sparse decomposition method. This method makes a sub-dictionary decomposition on the over-complete dictionary in the sparse decomposition. The sub-dictionary decomposition makes use of a novel criterion based on the occurrence frequency of atoms of the over-complete dictionary over the data set. The experimental results demonstrate that the dual-sparse-decomposition method surpasses state-of-art denoising performance in terms of both peak-signal-to-noise ratio and structural-similarity-index-metric, and also at subjective visual quality.

Chengwei Sang, Hong Sun, Quisong Xia

This letter presents a method of synthetic aperture radar (SAR) image despeckling aimed to preserve the detail information while suppressing speckle noise. This method combines the nonlocal self-similarity partition and a proposed modified sparse decomposition. The nonlocal partition method groups a series of structure-similarity data sets. Each data set has a good sparsity for learning an over-complete dictionary in sparse representation. In the sparse decomposition, we propose a novel method to identify principal atoms from over-complete dictionary to form a principal dictionary. Despeckling is performed on each data set over the principal dictionary with principal atoms. Experimental results demonstrate that the proposed method can achieve high performances in terms of both speckle noise reduction and structure details preservation.

Hong Sun, Chengwei Sang, Didier Le Ruyet

This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called "Sparse Signal Subspace Decomposition" (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, well adapted to subspace-decomposition over a dependent basis set, adequately re ects the intrinsic characteristic of regularity of the signal. The 3SD method combines variance, sparsity and component frequency criteria into an unified framework. It takes benefits from using an over-complete dictionary which preserves details and from subspace decomposition which rejects strong noise. The 3SD method is very simple with a linear retrieval operation. It does not require any prior knowledge on distributions or parameters. When applied to image denoising, it demonstrates high performances both at preserving fine details and suppressing strong noise.

Hong Sun, Cheng-Wei Sang, Chen-Guang Liu

This article introduces a new signal analysis method, which can be interpreted as a principal component analysis in sparse decomposition of the signal. The method, called principal basis analysis, is based on a novel criterion: reproducibility of component which is an intrinsic characteristic of regularity in natural signals. We show how to measure reproducibility. Then we present the principal basis analysis method, which chooses, in a sparse representation of the signal, the components optimizing the reproducibility degree to build the so-called principal basis. With this principal basis, we show that the underlying signal pattern could be effectively extracted from corrupted data. As illustration, we apply the principal basis analysis to image denoising corrupted by Gaussian and non-Gaussian noises, showing better performances than some reference methods at suppressing strong noise and at preserving signal details.