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In this paper, we present a unified frame based on collaborative representation (CR) for single-image super-resolution (SR), which learns low-resolution (LR) and high-resolution (HR) dictionaries independently in the training stage and adopts a consistent coding scheme (CCS) to guarantee the prediction accuracy of HR coding coefficients during SR reconstruction. The independent LR and HR dictionaries are learned based on CR with l 2 -norm regularization, which can well describe the corresponding LR and HR patch space, respectively. Furthermore, a mapping function is learned to map LR coding coefficients onto the corresponding HR coding coefficients. Propagation filtering can achieve smoothing over an image while preserving image context like edges or textural regions. Moreover, to preserve the edge structures of a super-resolved image and suppress artifacts, a propagation filtering-based constraint and image nonlocal self-similarity regularization are introduced into the SR reconstruction framework. Experimental comparison with state-of-the-art single image SR algorithms validates the effectiveness of proposed approach.
Yang et al. (Fri,) studied this question.