Diverse branch feature refinement network for efficient multi-scale super-resolution

Despite the existence of various super-resolution (SR) methods, most of them focus on designing models for specific upscaling factors rather than fully exploiting inter-scale correlation to improve efficiency. In contrast, multi-scale SR methods can effectively reduce the redundancy of network parameters by aggregating the feature extraction processes corresponding to multiple scales into a unified process. The aim of this study is to enhance the compactness and efficiency of the SR model. Thus, an efficient multi-scale SR method called the diverse branch feature refinement network (DBFRN) is proposed. By decoupling the training process and inference process based on the idea of structural re-parameterization, multi-branch topology is adopted to enrich multi-scale learning and merge branches to achieve efficient inference with equivalent effects. Specifically, two re-parameterization strategies are designed and two corresponding feature refinement blocks for different feature levels in multi-scale SR network. Extensive experiments demonstrate that the proposed multi-scale SR method is effective and efficient, and it can outperform advanced single-scale methods in terms of quantity and quality. By decoupling the training process and inference process based on the idea of structural re-parameterization, multi-branch topology is adopted to enrich multi-scale learning and merge branches to achieve efficient inference with equivalent effects. image