Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration

Image restoration (IR), which aims to recover high-quality images from degraded inputs, is a crucial task in modern image processing. Recent advancements in deep learning, particularly with Convolutional Neural Networks (CNNs) and Transformers, have significantly improved image restoration performance. However, existing methods lack a unified training benchmark that specifies the training iterations and configurations. Additionally, we construct an image complexity evaluation metric using the gray-level co-occurrence matrix (GLCM) and find that there exist a bias between the image complexity distributions of commonly used IR training and testing datasets, leading to suboptimal restoration results. Therefore, we construct a new large-scale IR dataset called ReSyn, that utilizes a novel image filtering method based on image complexity to achieve a balanced image complexity distribution, and contains both real and AIGC synthetic images. From the perspective of measuring the model's convergence ability and restoration capability, we construct a unified training standard that specifies the training iterations and configurations for image restoration models. Furthermore, we explore how to enhance the performance of transformer-based image restoration models based on linear attention mechanism. We propose RWKV-IR, a novel image restoration model that incorporates the linear complexity RWKV into the transformer-based image restoration structure, and enables both global and local receptive fields. Instead of directly integrating the Vision-RWKV into the transformer architecture, we replace the original Q-Shift in RWKV with a novel Depth-wise Convolution shift, which effectively models the local dependencies, and is further combined with Bi-directional attention to achieve both global and local aware linear attention. Moreover, we propose a Cross-Bi-WKV module that combines two Bi-WKV modules with different scanning orders to achieve a balanced attention for horizontal and vertical directions. Extensive quantitative and qualitative experiments demonstrate the effectiveness and competitive performance of our RWKV-IR model.