Missing intensity restoration via adaptive selection of perceptually optimized subspaces

Abstract

A missing intensity restoration method via adaptive selection of perceptually optimized subspaces is presented in this paper. In order to realize adaptive and perceptually optimized restoration, the proposed method generates several subspaces of known textures optimized in terms of the structural similarity (SSIM) index. Furthermore, the SSIM-based missing intensity restoration is performed by a projection onto convex sets (POCS) algorithm whose constraints are the obtained subspace and known intensities within the target image. In this approach, a non-convex maximization problem for calculating the projection onto the subspace is reformulated as a quasi-convex problem, and the restoration of the missing intensities becomes feasible. Furthermore, the selection of the optimal subspace is realized by monitoring the SSIM index converged in the POCS algorithm, and the adaptive restoration becomes feasible. Experimental results show that our method outperforms existing methods.