LIR: A Lightweight Baseline for Image Restoration

Recently, there have been significant advancements in Image Restoration based on CNN and transformer. However, the inherent characteristics of the Image Restoration task are often overlooked. Many works, instead, only focus on the basic block design and stack numerous such blocks to the model, leading to parameters redundant and computations unnecessary. Thus, the efficiency of the image restoration is hindered. In this paper, we propose a Lightweight Baseline for Image Restoration called LIR to efficiently reconstruct the image and remove degradations (blur, rain, noise, haze). First of all, LIR addresses the degradations existing in the local and global residual connections that are ignored by modern networks, through a simple structural design. Then, to achieve lightweight, a Lightweight Adaptive Attention (LAA) Block is introduced depending on the inherent characteristics of the Image Restoration, which is mainly composed of proposed Adaptive Filters and Attention Blocks. LAA is capable of adaptively sharpening contours, removing degradation, and capturing global information in various Image Restoration scenes in a computation-friendly manner. Extensive experiments demonstrate that our LIR achieves comparable performance to state-of-the-art models with fewer parameters and computations in certain tasks. In addition, it is worth noting that our LIR produces better visual results than state-of-the-art networks that are more in line with the human aesthetic.