DCS-RISR：用于有效的真实世界图像超分辨率的动态通道分裂
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution
摘要：
真实世界图像超分辨率（RISR）已收到越来越多的关注，以改善未知的复杂退化下的SR图像的质量。现有的方法依赖于重SR模型来增强不同退化水平的低分辨率（LR）图像，这大大限制了它们在资源有限的设备上的实际部署。在本文中，我们提出了一种新的动态通道分裂方案，有效的现实世界的图像超分辨率，称为DCS-RISR。具体来说，我们首先引入光退化预测网络来回归退化向量以模拟真实世界的退化，在此基础上生成通道分裂向量作为有效SR模型的输入。然后，提出了一种可学习的倍频程卷积块，以自适应地决定每个块处的低频和高频特征的信道分裂尺度，通过向低频特征提供大尺度而向高频特征提供小尺度来减少计算开销和存储成本。为了进一步提高RISR的性能，非局部正则化被用来补充来自LR和HR子空间的补丁知识，并具有自由计算的推理。大量的实验证明了DCS-RISR在不同基准数据集上的有效性。我们的DCS-RISR不仅实现了计算/参数和PSNR/SSIM度量之间的最佳权衡，而且还有效地处理具有不同退化水平的真实世界图像。
英文摘要：
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light degradation prediction network to regress the degradation vector to simulate the real-world degradations, upon which the channel splitting vector is generated as the input for an efficient SR model. Then, a learnable octave convolution block is proposed to adaptively decide the channel splitting scale for low- and high-frequency features at each block, reducing computation overhead and memory cost by offering the large scale to low-frequency features and the small scale to the high ones. To further improve the RISR performance, Non-local regularization is employed to supplement the knowledge of patches from LR and HR subspace with free-computation inference. Extensive experiments demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our DCS-RISR not only achieves the best trade-off between computation/parameter and PSNR/SSIM metric, and also effectively handles real-world images with different degradation levels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
