探索图像复原中的真实与合成数据集及线性注意力
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration
AI总结：
针对图像复原领域缺乏统一训练基准、数据集复杂度分布不均的问题，提出均衡的ReSyn数据集与统一训练标准，同时设计融合线性注意力的RWKV-IR模型，经实验验证有效提升复原性能。
摘要：
图像复原（IR）旨在从退化输入中恢复高质量图像，近期深度学习的发展显著提升了其性能。然而，现有方法缺乏统一的迭代次数与配置训练基准。我们还发现，常用的IR训练与测试数据集之间存在图像复杂度分布偏差，导致复原效果欠佳。为解决这一问题，我们提出了一个名为ReSyn的大规模IR数据集，该数据集采用一种基于图像复杂度的新型图像过滤方法以确保分布均衡，同时包含真实图像与AIGC合成图像。我们建立了统一的训练标准，明确了图像复原模型的迭代次数与配置，重点用于衡量模型的收敛性与复原能力。此外，我们利用线性注意力机制改进基于transformer的图像复原模型，提出RWKV-IR，将具有线性复杂度的RWKV集成到transformer结构中，可同时实现全局与局部感受野。我们并未直接使用Vision-RWKV，而是用深度卷积移位替换RWKV中原有的Q-Shift，以更好地建模局部依赖关系，并结合双向注意力实现全面的线性注意力。我们还提出了Cross-Bi-WKV模块，该模块融合两个具有不同扫描顺序的Bi-WKV模块，以实现水平与垂直注意力的均衡。大量实验验证了我们的RWKV-IR模型的有效性。
英文摘要：
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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