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Image Representation Using a Sparsely Sampled Codebook for Super-Resolution
Abstract
In this chapter, the authors propose a Super-Resolution (SR) method using a vector quantization codebook and filter dictionary. In the process of SR, we use the idea of compressive sensing to represent a sparsely sampled signal under the assumption that a combination of a small number of codewords can represent an image patch. A low-resolution image is obtained from an original high-resolution image, degraded by blurring and down-sampling. The authors propose a resolution enhancement using an alternative l1 norm minimization to overcome the convexity of l0 norm and the sparsity of l1 norm at the same time, where an iterative reweighted l1 norm minimization is used for optimization. After the reconstruction stage, because the optimization is implemented on image patch basis, an additional deblurring or denoising step is used to globally enhance the image quality. Experiment results show that the proposed SR method provides highly efficient results.
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