Paper Info
Title
D-3: Deep Dual-Domain Based Fast Restoration Of Jpeg-Compressed Images
Abstract
In this paper, we design a Deep Dual-Domain (D-3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3 model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.
Year
DOI
Venue
2016
10.1109/CVPR.2016.302
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Field
DocType
Volume
Computer vision,Pattern recognition,Inference,Neural coding,Computer science,JPEG,Artificial intelligence,Jpeg compression
Conference
2016
Issue
ISSN
Citations 
1
1063-6919
17
PageRank 
References 
Authors
0.64
8
6
Name
Order
Citations
PageRank
Zhangyang Wang143775.27
Ding Liu261132.97
Shiyu Chang377051.07
Qing Ling496860.48
Yingzhen Yang510614.62
Thomas S. Huang6278152618.42