Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics, but also manually generate low-resolution pairs of images that do not have sufficient confidence. In this work, we propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. This simplifies the complexity of algorithms based on a coarse-to-fine scheme. To alleviate restoration defects impacting detail information brought about by using a multi-scale architecture, we combine the characteristics of real-world blurring trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images. In conclusion, we propose a multi-scale network with a learnable discrete wavelet transform (MLWNet), which exhibits state-of-the-art performance on multiple real-world deblurred datasets, in terms of both subjective and objective quality as well as computational efficiency.

提出了一种基于单输入多输出（SIMO）的多尺度网络用于运动去模糊，简化了基于粗糙到精细方案的算法复杂性，并结合可学习的小波变换模块来减轻多尺度架构带来的细节信息恢复缺陷，该多尺度网络在多个真实去模糊数据集上表现出了最先进的主观和客观质量以及计算效率。

用可学习的离散小波变换的高效多尺度网络用于盲运动去模糊