Iterative Weighted DCT-SVD for Compressive Imaging

This paper proposes iterative weighted discrete cosine transform and singular value decomposition (DCT-SVD) transform for compressive sensing (CS) reconstruction. The idea of weight utilizes the priori that the components of the transform representation of an image usually are unequally important. Sequentially, larger weights are assigned to more important components to improve reconstruction quality. Besides, iterative DCT-SVD can be regarded as a sequence of adaptive transforms. DCT starts a recovery procedure as an initial transform. SVD is then performed on previous reconstruction to obtain a pair of transform bases for next recovery, and the mechanism is repeated until the reconstructions remain unchanged. The proposal does not introduce extra cost to CS sampling, but improves reconstruction quality much according to the numerical simulations.