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 base on a simple U-Net architecture, which removes or replaces unnecessary nonlinear activation functions to achieve the above balance with low system complexity. Specifically, we propose a feature fusion middleware (FFM) 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. In addition, we propose a multi-head attention middle block (MHAMB) as a bridge between the encoder and decoder to capture more global information and surpass the limitations of the receptive field of CNNs. 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.

翻译：图像复原旨在从退化输入图像（如去模糊和去雨）中获取高质量图像。在图像复原中，通常需要在空间细节与上下文信息之间维持复杂的平衡。尽管多级网络能够优化平衡这些相互竞争的目标并实现显著性能，但这也会增加系统的复杂性。本文提出了一种基于简单U-Net架构的山形单级设计，通过移除或替换不必要的非线性激活函数，以低系统复杂性实现上述平衡。具体而言，我们提出了一种特征融合中间件（FFM）机制，作为编码器-解码器架构层级间的信息交换组件。该机制将上层信息无缝集成到相邻下层，并依次传递至最底层。最终，所有信息融合到原始图像分辨率操作层，从而保留空间细节并整合上下文信息，确保高质量图像复原。此外，我们提出了一种多头注意力中间模块（MHAMB），作为编码器与解码器之间的桥梁，以捕获更多全局信息并突破CNN感受野的限制。大量实验表明，我们的方法（命名为M3SNet）在图像去雨和去模糊等多种图像复原任务中，以不到一半的计算成本超越了先前最先进的模型。