Coupled adversarial learning for single image super-resolution

Chih Chung Hsu, Kuan Yu Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Generative adversarial nets (GAN) have been widely used in several image restoration tasks such as image denoise, enhancement, and super-resolution. The objective functions of an image super-resolution problem based on GANs usually are reconstruction error, semantic feature distance, and GAN loss. In general, semantic feature distance was used to measure the feature similarity between the super-resolved and ground-truth images, to ensure they have similar feature representations. However, the feature is usually extracted by the pre-trained model, in which the feature representation is not designed for distinguishing the extracted features from low-resolution and high-resolution images. In this study, a coupled adversarial net (CAN) based on Siamese Network Structure is proposed, to improve the effectiveness of the feature extraction. In the proposed CAN, we offer GAN loss and semantic feature distances simultaneously, reducing the training complexity as well as improving the performance. Extensive experiments conducted that the proposed CAN is effective and efficient, compared to state-of-the-art image super-resolution schemes.

Original languageEnglish
Title of host publication2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728119465
DOIs
Publication statusPublished - 2020 Jun
Event11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020 - Hangzhou, China
Duration: 2020 Jun 82020 Jun 11

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2020-June
ISSN (Electronic)2151-870X

Conference

Conference11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
Country/TerritoryChina
CityHangzhou
Period20-06-0820-06-11

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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