Adaptive Feature Selection Modulation Network for Efficient Image Super-Resolution

In the realm of image super-resolution, learning-based methods have made significant progress. However, limited computational resources still restrict their application. This prompts us to develop an efficient method for achieving effective image super-resolution. In this letter, we propose a novel adaptive feature selection modulation network (AFSMNet) tailored for efficient image super-resolution. Specifically, we design feature modulation blocks, which include the adaptive feature selection modulation (AFSM) module and the self-gating feed-forward network (SFN). The AFSM module dynamically computes the importance of each feature channel. For channels with differing levels of importance, we employ distinct processing strategies, thereby concentrating the computational resources of the network on the more critical features as much as possible. This approach facilitates the maintenance of a low computational cost without compromising performance. The SFN restricts the flow of irrelevant feature information within the network through a simple gating mechanism. In this way, our method achieves efficient and effective image super-resolution. Extensive experiment results show that the proposed method achieves a better trade-off between reconstruction performance and computational efficiency compared to the current state-of-the-art lightweight super-resolution methods.