Blind Super Resolution with Reference Images and Implicit Degradation Representation

• URL: http://arxiv.org/abs/2507.13915v1
• Date: Fri, 18 Jul 2025 13:45:04 GMT
• Title: Blind Super Resolution with Reference Images and Implicit Degradation Representation
• Authors: Huu-Phu Do, Po-Chih Hu, Hao-Chien Hsueh, Che-Kai Liu, Vu-Hoang Tran, Ching-Chun Huang,
• Abstract summary: Degradation kernels should account for not only the degradation process but also the downscaling factor.<n>Applying the same degradation kernel across varying super-resolution scales may be impractical.<n>Our research acknowledges degradation kernels and scaling factors as pivotal elements for the BSR task.
• Score: 5.34372866210952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous studies in blind super-resolution (BSR) have primarily concentrated on estimating degradation kernels directly from low-resolution (LR) inputs to enhance super-resolution. However, these degradation kernels, which model the transition from a high-resolution (HR) image to its LR version, should account for not only the degradation process but also the downscaling factor. Applying the same degradation kernel across varying super-resolution scales may be impractical. Our research acknowledges degradation kernels and scaling factors as pivotal elements for the BSR task and introduces a novel strategy that utilizes HR images as references to establish scale-aware degradation kernels. By employing content-irrelevant HR reference images alongside the target LR image, our model adaptively discerns the degradation process. It is then applied to generate additional LR-HR pairs through down-sampling the HR reference images, which are keys to improving the SR performance. Our reference-based training procedure is applicable to proficiently trained blind SR models and zero-shot blind SR methods, consistently outperforming previous methods in both scenarios. This dual consideration of blur kernels and scaling factors, coupled with the use of a reference image, contributes to the effectiveness of our approach in blind super-resolution tasks.

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