24 August 2010 Remote sensing image restoration based on compressive sensing and two-step iteration shrinkage algorithm
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Abstract
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.
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Mingyi He, Mingyi He, Weihua Liu, Weihua Liu, Lin Bai, Lin Bai, } "Remote sensing image restoration based on compressive sensing and two-step iteration shrinkage algorithm", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100E (24 August 2010); doi: 10.1117/12.863151; https://doi.org/10.1117/12.863151
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