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

替代計量分析
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其它資訊
摘要Lossy 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).
著者Dong C., Deng Y., Loy C.C., Tang X.
會議名稱15th IEEE International Conference on Computer Vision, ICCV 2015
會議開始日11.12.2015
會議完結日18.12.2015
會議地點Santiago
會議國家/地區智利共和國
出版年份2016
月份2
日期17
卷號11-18-December-2015
頁次576 - 584
國際標準書號9781467383912
國際標準期刊號1550-5499
語言英式英語

上次更新時間 2020-06-08 於 02:03