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• W1508652512 abstract "A highly compressed image is usually not only of low resolution, but also suffers from compression artifacts (blocking artifact is treated as an example in this paper). Directly performing image super-resolution (SR) to a highly compressed image would also simultaneously magnify the blocking artifacts, resulting in an unpleasing visual experience. In this paper, we propose a novel learning-based framework to achieve joint single-image SR and deblocking for a highly-compressed image. We argue that individually performing deblocking and SR (i.e., deblocking followed by SR, or SR followed by deblocking) on a highly compressed image usually cannot achieve a satisfactory visual quality. In our method, we propose to learn image sparse representations for modeling the relationship between low- and high-resolution image patches in terms of the learned dictionaries for image patches with and without blocking artifacts, respectively . As a result, image SR and deblocking can be simultaneously achieved via sparse representation and morphological component analysis (MCA)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm." @default.
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• W1508652512 date "2015-07-01" @default.
• W1508652512 modified "2023-10-18" @default.
• W1508652512 title "Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image" @default.
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• W1508652512 doi "https://doi.org/10.1109/tmm.2015.2434216" @default.
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