运用压缩感知理论的图像稀疏表示与重建

doi:10.3969/j.issn.0255-8297.2014.05.002

Abstract

Abstract: Application of compressed sensing to sparse reconstruction of image is discussed. An orthogonal matching pursuit algorithm for reconstruction and Gaussian random matrix for measurement are used. We analyze and compare DCT and DWT both theoretically and experimentally. By adjusting the sub-block size and sampling rate of the experimental images, we make a comprehensive comparison of sub-block size,sampling rate and influences of the two algorithms on effectiveness and efficiency of sparse reconstruction in terms of runtime, reconstruction error and visual effects. In sparse image representation, DCT exhibits better overall performance than DWT. In order to achieve an optimal balance between reconstruction effectiveness and efficiency, a reasonable choice of sub-block size and sampling rate is required.

Key words: compressed sensing, sparse representation, image reconstruction, orthogonal matching pursuit

CLC Number:

TP391

FENG Xiang1,2, WAN Wang-gen1,2. Sparse Representation and Reconstruction of Image Based on Compressed Sensing[J]. Journal of Applied Sciences, 2014, 32(5): 447-452.

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Sparse Representation and Reconstruction of Image Based on Compressed Sensing

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