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. They, instead, tend to 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 network for Image Restoration called LIR to efficiently restore the image and remove degradations. First of all, through an ingenious structural design, LIR removes the degradations existing in the local and global residual connections that are ignored by modern networks. Then, a Lightweight Adaptive Attention (LAA) Block is introduced which is mainly composed of proposed Adaptive Filters and Attention Blocks. The proposed Adaptive Filter is used to adaptively extract high-frequency information and enhance object contours in various IR tasks, and Attention Block involves a novel Patch Attention module to approximate the self-attention part of the transformer. On the deraining task, our LIR achieves the state-of-the-art Structure Similarity Index Measure (SSIM) and comparable performance to state-of-the-art models on Peak Signal-to-Noise Ratio (PSNR). For denoising, dehazing, and deblurring tasks, LIR also achieves a comparable performance to state-of-the-art models with a parameter size of about 30\%. In addition, it is worth noting that our LIR produces better visual results that are more in line with the human aesthetic.

翻译：近年来，基于CNN和Transformer的图像复原技术取得了显著进展。然而，许多研究工作往往忽视了图像复原任务的内在特性，转而侧重于基础模块设计，并将大量此类模块堆叠到模型中，导致参数冗余和计算资源浪费，从而制约了图像复原的效率。本文提出一种名为LIR的轻量级图像复原基线网络，旨在高效恢复图像并消除退化效应。首先，通过精巧的结构设计，LIR消除了现代网络中普遍忽视的局部与全局残差连接中存在的退化问题。随后，我们引入了轻量级自适应注意力（LAA）模块，该模块主要由提出的自适应滤波器与注意力模块构成。所提出的自适应滤波器能够自适应地提取高频信息，并在各类图像复原任务中增强物体轮廓；而注意力模块则包含一种新颖的块注意力机制，用于近似Transformer中的自注意力部分。在去雨任务中，我们的LIR在结构相似性指数（SSIM）上达到了最优水平，在峰值信噪比（PSNR）指标上也取得了与最优模型相当的性能。对于去噪、去雾和去模糊任务，LIR仅需约30%的参数规模即可达到与最优模型相当的性能。此外值得指出的是，我们的LIR能够生成更符合人类审美标准的视觉结果。