DocumentCode :
3748507
Title :
Compression Artifacts Reduction by a Deep Convolutional Network
Author :
Chao Dong;Yubin Deng;Chen Change Loy;Xiaoou Tang
Author_Institution :
Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
fYear :
2015
Firstpage :
576
Lastpage :
584
Abstract :
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).
Keywords :
"Image coding","Feature extraction","Image restoration","Image resolution","Noise measurement","Image reconstruction","Transform coding"
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2015 IEEE International Conference on
Electronic_ISBN :
2380-7504
Type :
conf
DOI :
10.1109/ICCV.2015.73
Filename :
7410430
Link To Document :
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