Deep learning-based motion deblurring techniques have advanced significantly in recent years. This class of techniques, however, does not carefully examine the inherent flaws in blurry images. For instance, low edge and structural information are traits of blurry images. The high-frequency component of blurry images is edge information, and the low-frequency component is structure information. A blind motion deblurring network (MCMS) based on multi-category information and multi-scale stripe attention mechanism is proposed. Given the respective characteristics of the high-frequency and low-frequency components, a three-stage encoder-decoder model is designed. Specifically, the first stage focuses on extracting the features of the high-frequency component, the second stage concentrates on extracting the features of the low-frequency component, and the third stage integrates the extracted low-frequency component features, the extracted high-frequency component features, and the original blurred image in order to recover the final clear image. As a result, the model effectively improves motion deblurring by fusing the edge information of the high-frequency component and the structural information of the low-frequency component. In addition, a grouped feature fusion technique is developed so as to achieve richer, more three-dimensional and comprehensive utilization of various types of features at a deep level. Next, a multi-scale stripe attention mechanism (MSSA) is designed, which effectively combines the anisotropy and multi-scale information of the image, a move that significantly enhances the capability of the deep model in feature representation. Large-scale comparative studies on various datasets show that the strategy in this paper works better than the recently published measures.

翻译：基于深度学习的运动去模糊技术在近年来取得了显著进展。然而，这类技术并未深入探究模糊图像固有的缺陷。例如，低边缘和结构信息是模糊图像的典型特征。模糊图像的高频分量是边缘信息，低频分量是结构信息。本文提出了一种基于多类别信息与多尺度条带注意力机制的盲运动去模糊网络（MCMS）。针对高频与低频分量的各自特性，设计了一个三阶段编码器-解码器模型。具体而言，第一阶段专注于提取高频分量的特征，第二阶段专注于提取低频分量的特征，第三阶段则整合提取到的低频分量特征、高频分量特征以及原始模糊图像，以恢复最终的清晰图像。因此，该模型通过融合高频分量的边缘信息和低频分量的结构信息，有效提升了运动去模糊效果。此外，为在深层实现更丰富、更立体、更全面的多类特征综合利用，本文提出了一种分组特征融合技术。接着，设计了一种多尺度条带注意力机制（MSSA），该机制能够有效结合图像的各向异性和多尺度信息，显著增强深度模型的特征表示能力。在多种数据集上的大规模对比研究表明，本文策略优于近期发表的方法。