LIR: A Lightweight Baseline for Image Restoration

• URL: http://arxiv.org/abs/2402.01368v3
• Date: Mon, 24 Jun 2024 06:27:44 GMT
• Title: LIR: A Lightweight Baseline for Image Restoration
• Authors: Dongqi Fan, Ting Yue, Xin Zhao, Renjing Xu, Liang Chang,
• Abstract summary: The inherent characteristics of the Image Restoration task are often overlooked in many works. We propose a Lightweight Baseline network for Image Restoration called LIR to efficiently restore the image and remove degradations. 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)
• Score: 4.187190284830909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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