Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

• URL: http://arxiv.org/abs/2412.03814v2
• Date: Wed, 11 Dec 2024 07:50:40 GMT
• Title: Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration
• Authors: Yuzhen Du, Teng Hu, Jiangning Zhang, Ran Yi Chengming Xu, Xiaobin Hu, Kai Wu, Donghao Luo, Yabiao Wang, Lizhuang Ma,
• Abstract summary: Image restoration aims to recover high-quality images from degraded inputs.<n>Existing methods lack a unified training benchmark for iterations and configurations.<n>We introduce a large-scale IR dataset called ReSyn, which employs a novel image filtering method based on image complexity.
• Score: 47.26304397935705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration (IR) aims to recover high-quality images from degraded inputs, with recent deep learning advancements significantly enhancing performance. However, existing methods lack a unified training benchmark for iterations and configurations. We also identify a bias in image complexity distributions between commonly used IR training and testing datasets, resulting in suboptimal restoration outcomes. To address this, we introduce a large-scale IR dataset called ReSyn, which employs a novel image filtering method based on image complexity to ensure a balanced distribution and includes both real and AIGC synthetic images. We establish a unified training standard that specifies iterations and configurations for image restoration models, focusing on measuring model convergence and restoration capability. Additionally, we enhance transformer-based image restoration models using linear attention mechanisms by proposing RWKV-IR, which integrates linear complexity RWKV into the transformer structure, allowing for both global and local receptive fields. Instead of directly using Vision-RWKV, we replace the original Q-Shift in RWKV with a Depth-wise Convolution shift to better model local dependencies, combined with Bi-directional attention for comprehensive linear attention. We also introduce a Cross-Bi-WKV module that merges two Bi-WKV modules with different scanning orders for balanced horizontal and vertical attention. Extensive experiments validate the effectiveness of our RWKV-IR model.

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