Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

Abstract

In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a 'residual formatting layer' to format the residual to structured information, which allows the network to converge faster and boosts the performance. Furthermore, we propose a cross-level loss net to ensure both pixel-level accuracy and semantic-level visual quality. Evaluations on public datasets show that the proposed method outperforms existing approaches quantitatively and qualitatively.

Keywords

Computer vision, Deep learning, Deep neural networks, Pattern recognition, Restoration, Semantics

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, Honolulu, Hawaii, USA, July 21-26

First Page

1034

Last Page

1042

ISBN

9781538607336

Identifier

10.1109/CVPRW.2017.140

Publisher

IEEE

City or Country

New York, NY, USA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CVPRW.2017.140

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