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 downsampled to 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 lightweight dual regression compression 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.

该论文研究了使用深度神经网络进行图像超分辨率（SR）的任务，并且提出了一种双重回归学习方案和轻量级双重回归压缩方法，以解决 SR 映射空间极大和模型冗余的问题。