MCMS：面向盲运动去模糊的多类别信息与多尺度条纹注意力机制
MCMS: Multi-Category Information and Multi-Scale Stripe Attention for Blind Motion Deblurring
AI总结：
本文提出盲运动去模糊网络MCMS，通过三阶段编解码器分别提取高、低频特征并融合，结合分组特征融合与多尺度条纹注意力机制MSSA，在多个数据集上取得优于近期方法的去模糊效果。
摘要：
近年来，基于深度学习的运动去模糊技术取得了显著进展。然而，这类技术并未仔细审视模糊图像中固有的缺陷。例如，低边缘信息和低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纹注意力机制的盲运动去模糊网络（MCMS）。考虑到高频分量和低频分量各自的特点，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，第二阶段集中于提取低频分量的特征，第三阶段将提取到的低频分量特征、提取到的高频分量特征以及原始模糊图像进行整合，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，有效改善了运动去模糊效果。此外，本文还开发了一种分组特征融合技术，以便在深层上实现各类特征更丰富、更立体和更全面的利用。接着，设计了一种多尺度条纹注意力机制（MSSA），该机制有效结合了图像的各向异性和多尺度信息，显著增强了深层模型在特征表示方面的能力。在多个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。
英文摘要：
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.
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