Blind Image Super-Resolution via Contrastive Representation Learning

• URL: http://arxiv.org/abs/2107.00708v1
• Date: Thu, 1 Jul 2021 19:34:23 GMT
• Title: Blind Image Super-Resolution via Contrastive Representation Learning
• Authors: Jiahui Zhang, Shijian Lu, Fangneng Zhan, Yingchen Yu
• Abstract summary: We design a contrastive representation learning network that focuses on blind SR of images with multi-modal and spatially variant distributions. We show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings. It also outperforms state-of-the-art SR methods qualitatively and quantitatively.
• Score: 41.17072720686262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image super-resolution (SR) research has witnessed impressive progress thanks to the advance of convolutional neural networks (CNNs) in recent years. However, most existing SR methods are non-blind and assume that degradation has a single fixed and known distribution (e.g., bicubic) which struggle while handling degradation in real-world data that usually follows a multi-modal, spatially variant, and unknown distribution. The recent blind SR studies address this issue via degradation estimation, but they do not generalize well to multi-source degradation and cannot handle spatially variant degradation. We design CRL-SR, a contrastive representation learning network that focuses on blind SR of images with multi-modal and spatially variant distributions. CRL-SR addresses the blind SR challenges from two perspectives. The first is contrastive decoupling encoding which introduces contrastive learning to extract resolution-invariant embedding and discard resolution-variant embedding under the guidance of a bidirectional contrastive loss. The second is contrastive feature refinement which generates lost or corrupted high-frequency details under the guidance of a conditional contrastive loss. Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.

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