Compression artifacts reduction by a deep convolutional network
Refereed conference paper presented and published in conference proceedings


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AbstractLossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects. Inspired by the deep convolutional networks (DCN) on super-resolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. We also demonstrate that a deeper model can be effectively trained with the features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate several practical transfer settings and show the effectiveness of transfer learning in low level vision problems. Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use cases (i.e. Twitter).
All Author(s) ListDong C., Deng Y., Loy C.C., Tang X.
Name of Conference15th IEEE International Conference on Computer Vision, ICCV 2015
Start Date of Conference11/12/2015
End Date of Conference18/12/2015
Place of ConferenceSantiago
Country/Region of ConferenceRepublic of Chile
Year2016
Month2
Day17
Volume Number11-18-December-2015
Pages576 - 584
ISBN9781467383912
ISSN1550-5499
LanguagesEnglish-United Kingdom

Last updated on 2020-05-07 at 00:43