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Deep neural de-raining model based on dynamic fusion of multiple vision tasks
Soft Computing ( IF 4.1 ) Pub Date : 2020-09-08 , DOI: 10.1007/s00500-020-05291-y
Yulong Fan , Rong Chen , Yang Li , Tianlun Zhang

Image quality is relevant to the performance of computer vision applications. The interference of rain streaks often greatly depreciates the visual effect of images. It is a traditional and critical vision challenge to remove rain streaks from rainy images. In this paper, we introduce a deep connectionist screen blend model for single-image rain removal research. The novel deep structure is mainly composed of shortcut connections, and ends with sibling branches. The specific architecture is designed for joint optimization of heterogeneous but related tasks. In particular, a feature-level task is design to preserve object edges which tend to be lost in de-rained images. Moreover, a comprehensive image quality assessment is an additional vision task for further improvement on de-rained results. Instead of using rules of thumb, we propose an actionable method to dynamically assign appropriate weighting coefficients for all vision tasks we use. On the other hand, various factors such as haze also give rise to weak visual appeal of rainy images. To remove these adverse factors, we develop an image enhancement framework which enables the hyperparameters to be optimized in an adaptive way, and efficiently improves the perceived quality of de-rained results. The effectiveness of the proposed de-raining system has been verified by extensive experiments, and most results of our method are impressive. The source code and more de-rained results will be available online.



中文翻译:

基于多视觉任务动态融合的深层神经训练模型

图像质量与计算机视觉应用程序的性能有关。雨水条纹的干扰通常会大大降低图像的视觉效果。从多雨图像中去除雨水条纹是一项传统且至关重要的视觉挑战。在本文中,我们介绍了用于单图像除雨研究的深度连接器屏幕混合模型。新颖的深层结构主要由快捷连接组成,并以同级分支结尾。该特定体系结构设计用于联合优化异构但相关的任务。特别是,设计了一个功能级别的任务来保留对象边缘,这些对象边缘往往会在掉雨的图像中丢失。此外,全面的图像质量评估是进一步改善排水效果的视觉任务。与其使用经验法则,我们提出了一种可行的方法,可以为我们使用的所有视觉任务动态分配适当的加权系数。另一方面,诸如雾度之类的各种因素也导致雨天图像的视觉吸引力减弱。为了消除这些不利因素,我们开发了一种图像增强框架,该框架使超参数能够以自适应方式进行优化,并有效地提高了排水效果的感知质量。所提出的除雨系统的有效性已通过大量实验验证,我们方法的大多数结果令人印象深刻。源代码和更轻松的结果将在线提供。为了消除这些不利因素,我们开发了一种图像增强框架,该框架使超参数能够以自适应方式进行优化,并有效地提高了排水效果的感知质量。所提出的除雨系统的有效性已通过大量实验验证,我们方法的大多数结果令人印象深刻。源代码和更轻松的结果将在线提供。为了消除这些不利因素,我们开发了一种图像增强框架,该框架使超参数能够以自适应方式进行优化,并有效地提高了排水效果的感知质量。所提出的除雨系统的有效性已通过大量实验验证,我们方法的大多数结果令人印象深刻。源代码和更轻松的结果将在线提供。

更新日期:2020-09-08
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