Image restoration is a long-standing task that seeks to recover the latent sharp image from its deteriorated counterpart. Due to the robust capacity of self-attention to capture long-range dependencies, transformer-based methods or some attention-based convolutional neural networks have demonstrated promising results on many image restoration tasks in recent years. However, existing attention modules encounters limited receptive fields or abundant parameters. In order to integrate contextual information more effectively and efficiently, in this paper, we propose a dilated strip attention network (DSAN) for image restoration. Specifically, to gather more contextual information for each pixel from its neighboring pixels in the same row or column, a dilated strip attention (DSA) mechanism is elaborately proposed. By employing the DSA operation horizontally and vertically, each location can harvest the contextual information from a much wider region. In addition, we utilize multi-scale receptive fields across different feature groups in DSA to improve representation learning. Extensive experiments show that our DSAN outperforms state-of-the-art algorithms on several image restoration tasks.

翻译：图像复原是一项长期存在的任务，旨在从退化的图像中恢复潜在的清晰图像。由于自注意力机制在捕获长程依赖方面具有强大能力，基于Transformer的方法或一些基于注意力的卷积神经网络近年来在许多图像复原任务中展现了有前景的结果。然而，现有的注意力模块存在感受野有限或参数量过大的问题。为了更有效且高效地整合上下文信息，本文提出了一种用于图像复原的空洞条带注意力网络（DSAN）。具体而言，为了从同一行或同一列的相邻像素中为每个像素收集更多上下文信息，我们精心设计了一种空洞条带注意力（DSA）机制。通过水平和垂直方向应用DSA操作，每个位置可以从更广泛的区域获取上下文信息。此外，我们在DSA中利用跨不同特征组的多尺度感受野来改进表示学习。大量实验表明，我们的DSAN在多项图像复原任务上优于现有最先进的算法。