Sparse-filtering in directional lifting wavelet transform domain based Bayesian compressive sensing

作者：Hou Xingsong^*; Zhang Lan; Chen Zan; Gong Chen

来源：International Journal of Wavelets, Multiresolution and Information Processing, 2014, 12(6): 1450043.

DOI：10.1142/S021969131450043X

• Bayesian compressive sensing
• DLWT
• sparse-filtering
• tail-folding
• interscale dependency

Compressive sensing (CS) has been proposed for images that are sparse under a certain transform domain. However, many natural images are not strictly sparse in the transform domain, causing a tail-folding effect that degrades the performance of the CS reconstruction. To decrease such effect, a sparse-filtering (SF) in Directional Lifting Wavelet Transform (DLWT) domain based Bayesian compressive sensing (BCS) algorithm (DLWT-SF-TSW-BCS) is proposed. At the encoder, DLWT, an efficient multi-scale geometrical analysis (MGA) tool, is used to produce the sparse representation for natural images. Then sparse-filtering is adopted to cut off the small DLWT coefficients before random measurement. At the decoder, the interscale tree-structure redundancy in DLWT domain is further exploited in Bayesian reconstruction. Experimental results show that the proposed DLWT-SF-TSW-BCS algorithm significantly outperforms other state-of-the-art CS reconstruction algorithms, for example, peak signal to noise ratio (PSNR) gain up to 10.00 dB over the tree structured wavelet compressive sensing (TSW-CS).

• 出版日期2014-11
• 单位西安交通大学; 中国科学技术大学