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Sparse Image Reconstruction Using Improved Regularized Orthogonal Matching Pursuit

Meenakshi, Sumit Budhiraja

Abstract



Compressive Sensing (CS) utilizes the sparsity of images to reduce the number of measurements and is able to achieve reduction in amount of storage required; breaking the limitations of Nyquist Shannon theorem. CS based image reconstruction is capable of providing easy and accurate sparse reconstruction; though with longer running time. Regularised Orthogonal Matching Pursuit (ROMP) is one of the greedy algorithms which enable faster implementation of image recovery, but the performance of recovery is not satisfactory. In this paper, improved ROMP method is presented which works on low frequency coefficients of the image under certain stopping conditions; thus saving memory with reduced running time. The recovery process is followed by thresholding; so that significant elements are identified and smaller components are rejected. The simulation results for both noiseless and noisy cases show that this method provides better PSNR and faster means for sparse recovery from inaccurate and fewer measurements.

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