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• W2553668389 abstract "We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free image. Recent approaches for this problem typically train a one-to-one mapping using a per-pixel L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> loss between the outputs and the ground-truths. We point out that these approaches used to produce overly smooth results, and PSNR doesn't reflect their real performance. In this paper, we propose a one-to-many network, which measures output quality using a perceptual loss, a naturalness loss, and a JPEG loss. We also avoid grid-like artifacts during deconvolution using a shift-and-average strategy. Extensive experimental results demonstrate the dramatic visual improvement of our approach over the state of the arts." @default.
• W2553668389 created "2016-11-30" @default.
• W2553668389 creator A5051705800 @default.
• W2553668389 creator A5061525421 @default.
• W2553668389 date "2017-07-01" @default.
• W2553668389 modified "2023-09-30" @default.
• W2553668389 title "One-To-Many Network for Visually Pleasing Compression Artifacts Reduction" @default.
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• W2553668389 doi "https://doi.org/10.1109/cvpr.2017.517" @default.
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