Efficient Feature-Guided Approach for Image Restoration

In this paper, we propose a lightweight image restoration method that achieves computational efficiency through guided restoration and detail enhancement. Our approach introduces two key components. First, the Feature Pick (FP) module directs the restoration process by filtering out redundant features, reducing computational overhead. Second, the Detail Auxiliary Block (DAB) module enhances image details by dynamically adjusting weights, allowing finer details to bypass the main restoration network and alleviating its processing burden. Together, these modules significantly improve efficiency while maintaining high restoration quality. We evaluate our method on multiple image restoration tasks, including denoising and deblurring, demonstrating state-of-the-art performance with reduced computational cost. The source code and pretrained model are available at https://github.com/leisoul/FPNET.