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 in many works. These works often focus on the basic block design and stack numerous basic blocks to the model, leading to redundant parameters and unnecessary computations and hindering the efficiency of the image restoration. In this paper, we propose a Lightweight Image Restoration network called LIR to efficiently remove degradation (blur, rain, noise, haze, etc.). A key component in LIR is the Efficient Adaptive Attention (EAA) Block, which is mainly composed of Adaptive Filters and Attention Blocks. It is capable of adaptively sharpening contours, removing degradation, and capturing global information in various image restoration scenes in an efficient and computation-friendly manner. In addition, through a simple structural design, LIR addresses the degradations existing in the local and global residual connections that are ignored by modern networks. Extensive experiments demonstrate that our LIR achieves comparable performance to state-of-the-art networks on most benchmarks with fewer parameters and computations. 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.

翻译：近期，基于CNN和Transformer的图像修复技术取得了重要进展。然而，许多工作常忽略图像修复任务的固有特性，过度关注基础模块设计并堆叠大量基础模块，导致参数冗余和计算资源浪费，阻碍了图像修复的效率。本文提出名为LIR的轻量级图像修复网络，旨在高效去除（模糊、雨痕、噪声、雾霾等）退化现象。LIR的核心组件是高效自适应注意力模块（EAA Block），该模块主要由自适应滤波器和注意力模块构成，能够以高效且计算友好的方式自适应锐化轮廓、去除退化，并在多种图像修复场景中捕获全局信息。此外，通过简洁的结构设计，LIR解决了现代网络忽视的局部与全局残差连接中的退化问题。大量实验表明，在多数基准测试中，LIR以更少的参数和计算量取得了与现有先进网络相媲美的性能。值得关注的是，LIR生成的视觉结果更符合人类审美，优于当前先进网络。