Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR images that can be super-resolved from the same LR image. As a result, it is hard to directly learn a promising SR mapping from such a large space. Second, it is often inevitable to develop very large models with extremely high computational cost to yield promising SR performance. In practice, one can use model compression techniques to obtain compact models by reducing model redundancy. Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space. To alleviate the first challenge, we propose a dual regression learning scheme to reduce the space of possible SR mappings. Specifically, in addition to the mapping from LR to HR images, we learn an additional dual regression mapping to estimate the downsampling kernel and reconstruct LR images. In this way, the dual mapping acts as a constraint to reduce the space of possible mappings. To address the second challenge, we propose a dual regression compression (DRC) method to reduce model redundancy in both layer-level and channel-level based on channel pruning. Specifically, we first develop a channel number search method that minimizes the dual regression loss to determine the redundancy of each layer. Given the searched channel numbers, we further exploit the dual regression manner to evaluate the importance of channels and prune the redundant ones. Extensive experiments show the effectiveness of our method in obtaining accurate and efficient SR models.

翻译：深度神经网络通过学习从低分辨率图像到高分辨率图像的映射，在图像超分辨率任务中展现出卓越性能。然而，超分辨率问题通常是一个不适定问题，现有方法存在若干局限性。首先，由于同一低分辨率图像可能对应多个可超分辨率重建的高分辨率图像，超分辨率的可能映射空间极大，导致难以直接从此类大空间中学习到有效的超分辨率映射。其次，为获得理想的超分辨率性能，往往需要开发具有极高计算成本的大型模型。实践中可采用模型压缩技术，通过减少模型冗余获得紧凑模型。然而，现有模型压缩方法因超分辨率映射空间极大，难以准确识别冗余组件。针对第一个挑战，我们提出双重回归学习方案以缩小超分辨率映射空间。具体而言，除学习从低分辨率到高分辨率图像的映射外，我们额外学习一个双重回归映射来估计下采样核并重建低分辨率图像。通过这种方式，双重映射作为约束条件缩小了可能的映射空间。针对第二个挑战，我们提出基于通道剪枝的双重回归压缩方法，在层级和通道级两个层面减少模型冗余。具体而言，首先开发最小化双重回归损失的通道数搜索方法以确定每层冗余度。基于搜索到的通道数，进一步利用双重回归方式评估通道重要性并剪除冗余通道。大量实验证明，该方法能有效获得精确且高效的超分辨率模型。