Compressive Sensing of Image Reconstruction Using Multi-wavelet Transforms
By given a sparse signal in a high dimensional space, compressive sensing system, which combines with sampling and compression, can reconstruct that signal accurately and efficiently from fewer linear measurements much less than its actual dimension using sparse priors of signal. Currently, researchers always use orthogonal wavelet to represent the images. But the wavelet only has single scaling function and can not simultaneously satisfy the orthogonality, high vanishing moments, compact support, symmetry characteristic and regularity. Developed from the theory of wavelet, multi wavelet transform, which can simultaneously satisfy the five characteristics, provides a great potential to obtain high performance coding. According to the three main steps (Sparse representation, measurement matrix, reconstruction algorithm) of compressive sensing image reconstruction, this paper proposes a compressive sensing image reconstruction based on sparse representation of the image in multi-wavelet transform domain while using Orthogonal Matching Pursuit iterative as the reconstruction algorithm. The experimental results show that the reconstructed image has batter vision quality and a good performance on PSNR. Meanwhile, the algorithm of reconstruction gets a faster convergence rate.
Compressive sensing Multi-wavelet transform Restricted Isometry Property Orthogonal Matching Pursuit
Fan Yang Shengqian Wang Chengzhi Deng
Key Laboratory of Optic-electronic & Communication Jiangxi Science & Technology Normal University Na Department of Information Engineering Nanchang Institute of Technology Nanchang, China
国际会议
厦门
英文
702-705
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）