In this paper we use sparse-representation modeling for the single image scale-up problem. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to solve it in a robust fashion. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially adaptive and non-linear filters of various sorts.In this paper, we embark from a recently-proposed algorithm by Yang et. al. [1,2], and similarly assume a local Sparse-Land model on image patches, thus stabilizing the problem. We introduce several important modifications to the above-mentioned solution, and show that these lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and operating without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.
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