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论文类型： 期刊论文

发表时间： 2013-01-01

发表刊物： JOURNAL OF MATHEMATICAL IMAGING AND VISION

收录刊物： SCIE、EI、Scopus

卷号： 45

期号： 1

页面范围： 46-54

ISSN号： 0924-9907

关键字： Image denoising; Joint sparse representation; Sparse noise; KSVD

摘要： This paper addresses the recovery of original images from multiple copies corrupted with the noises, which can be represented sparsely in some dictionary. Sparse representation has been proven to have strong ability to denoise. However, it performs suboptimally when the noise is sparse in some dictionary. A novel joint sparse representation (JSR)-based image denoising method is proposed. The images can be recovered well from multiple noisy copies. All copies share a common component-the image, while each individual measurement contains an innovation component-the noise. Our method can separate the common and innovation components, and reconstruct the images with the sparse coefficients and the dictionaries. Experiment results show that the performance of the proposed method is better than that of other methods in terms of the metric and the visual quality.