A Practical Contrastive Learning Framework for Single-Image Super-Resolution

• URL: http://arxiv.org/abs/2111.13924v2
• Date: Sun, 16 Jul 2023 16:00:40 GMT
• Title: A Practical Contrastive Learning Framework for Single-Image Super-Resolution
• Authors: Gang Wu and Junjun Jiang and Xianming Liu
• Abstract summary: We investigate contrastive learning-based single image super-resolution from two perspectives. We propose a practical contrastive learning framework for SISR, named PCL-SR. Compared with existing benchmark methods, we re-train them by our proposed PCL-SR framework and achieve superior performance.
• Score: 51.422185656787285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challenging to adopt vanilla contrastive learning technologies proposed for high-level visual tasks to low-level image restoration problems straightly. Because the acquired high-level global visual representations are insufficient for low-level tasks requiring rich texture and context information. In this paper, we investigate the contrastive learning-based single image super-resolution from two perspectives: positive and negative sample construction and feature embedding. The existing methods take naive sample construction approaches (e.g., considering the low-quality input as a negative sample and the ground truth as a positive sample) and adopt a prior model (e.g., pre-trained VGG model) to obtain the feature embedding. To this end, we propose a practical contrastive learning framework for SISR, named PCL-SR. We involve the generation of many informative positive and hard negative samples in frequency space. Instead of utilizing an additional pre-trained network, we design a simple but effective embedding network inherited from the discriminator network which is more task-friendly. Compared with existing benchmark methods, we re-train them by our proposed PCL-SR framework and achieve superior performance. Extensive experiments have been conducted to show the effectiveness and technical contributions of our proposed PCL-SR thorough ablation studies. The code and pre-trained models can be found at https://github.com/Aitical/PCL-SISR.

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