关键词: 压缩感知, 多成分混合字典, 稀疏图像表示, 图像重构

Abstract: The precision of the signal reconstruction by compressed sensing is decided by the sparse degree of signal decomposition. According to the insufficient sparse degree of standard orthogonal basis, this paper establishes two-dimensional Gabor dictionaries which can match the edge and texture of an image. Mixed sparse decomposing of the image on the discrete cosine dictionary and established two-dimensional Gabor dictionaries, it can get the smooth component, edge component and texture component of the image. It measures the obtained sparse components by compressed sensing, by solving an optimization problem, original image can be reconstructed. Experimental results demonstrate that the multi-component dictionaries can give more sparse representation for an image, and the quality of image reconstruction is better than standard orthogonal basis sparse decomposition in the same sampling rate.

Key words: compressed sensing, multi-component dictionaries, sparse image representation, image reconstruction