This paper proposes a new regularization algorithm combining the wavelet-based and contourlet-based regularization items based on the Compressive Sensing (CS) theorem. The new algorithm aims at gaining maximum benefit by combining the multiscale and multiresolution properties common to both wavelet and contourlet schemes, while simultaneously incorporating their individual properties of point singularity and line singularity respectively. CS is applied to remote sensing image deblurring. It has great practical significance due to saving the hardware cost and aiding fast transmission. Experimental results show the method achieves improvement in peak-signal-noise-ratio and correlation function as compared to traditional regularization algorithms.

Date Published: 24 August 2010
PDF: 6 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100E (24 August 2010); doi: 10.1117/12.863151

Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)