Formnet: Formatted learning for image restoration

Publication Type

Journal Article

Publication Date

1-2020

Abstract

In this paper, we propose a deep CNN to tackle the image restoration problem by learning formatted information. 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 and an adversarial block to format the information to structured one, 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 performs favorably against existing approaches quantitatively and qualitatively.

Keywords

Image restoration, format, residual, GAN, CNN

Discipline

English Language and Literature

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Image Processing

Volume

29

First Page

6302

Last Page

6314

ISSN

1057-7149

Identifier

10.1109/TIP.2020.2990603

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIP.2020.2990603

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