IMAGE RECONSTRUCTION BASED ON COMPRESSIVE SAMPLING USING IRLS AND OMP ALGORITHM
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We proposed compressive sensing to reduce the sampling rate of the image and improve the accuracy of image reconstruction. Compressive sensing requires that the representation of the image is sparse on a certain basis. We use wavelet transformation to provide sparsity matrix basis. Meanwhile, to get a projection matrix using a random orthonormal process. The algorithm used to reconstruct the image is orthogonal matching pursuit (OMP) and Iteratively Reweighted Least Squares (IRLS). The test result indicates that a high quality image is obtained along with the number of coefficients M. IRLS has a good performance on PSNR than OMP while OMP takes the least time for reconstruction.
2016 ◽
Vol 76
(7)
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pp. 9265-9296
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1976 ◽
Vol 24
(8)
◽
pp. 529-531
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2012 ◽
Vol 56
(11)
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pp. 1-10
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