A novel single-stage network for accurate image restoration

Image restoration is the task of aiming to obtain a high-quality image from a corrupt input image, such as deblurring and deraining. In image restoration, it is typically necessary to maintain a complex balance between spatial details and contextual information. Although a multi-stage network can optimally balance these competing goals and achieve significant performance, this also increases the system’s complexity. In this paper, we propose a mountain-shaped single-stage design, which achieves the performance of multi-stage networks through a plug-and-play feature fusion middleware. Specifically, we propose a plug-and-play feature fusion middleware mechanism as an information exchange component between the encoder-decoder architectural levels. It seamlessly integrates upper-layer information into the adjacent lower layer, sequentially down to the lowest layer. Finally, all information is fused into the original image resolution manipulation level. This preserves spatial details and integrates contextual information, ensuring high-quality image restoration. Simultaneously, we propose a multi-head attention middle block as a bridge between the encoder and decoder to capture more global information and surpass the limitations of the receptive field of CNNs. In order to achieve low system complexity, we removes or replaces unnecessary nonlinear activation functions. Extensive experiments demonstrate that our approach, named as M3SNet, outperforms previous state-of-the-art models while using less than half the computational costs, for several image restoration tasks, such as image deraining and deblurring. The code and the pre-trained models will be released at https://github.com/Tombs98/M3SNet.