IMAGE SAMPLING AND RECONSTRUCTION USING COMPRESSIVE SENSING

There has been growing interest in unifying the fields of compressive sensing and sparse representations to perform imaging. In this paper, we have reviewed compressive sensing theory and studied the scheme of image sampling and reconstruction. The whole process measures a subset of the pixels in the photograph and uses compressive sensing algorithms to reconstruct the entire image from this data. We have also analyzed and compared the combination influences of various sensing and sparse transform matrices, subsampling rate and recovery algorithms. Experimental results are very encouraging and constructive, both visually and quantitatively. From the results, we have concluded that Restricted Isometry Condition (RIC) plays an important role in the quality of the reconstructive images. The results also clearly demonstrate the efficacy of the compressive sensing in image reconstruction.