Single Image Rain Streak Decomposition Using Layer Priors
Single Image Rain Streak Decomposition Using Layer Priors
复制标题
使用层先验的单图像雨条纹分解
DOI：
10.1109/tip.2017.2708841
复制
发表时间：
2017-08-01
影响因子：
10.6
通讯作者：
Brown, Michael S.
中科院分区：
文献类型：
--
作者：
Li, Yu;Tan, Robby T.;Brown, Michael S.
雨条纹会损害图像的可见性，并引入不期望的干扰，这会严重影响计算机视觉和图像分析系统的性能。雨带去除算法尝试恢复无雨带的背景场景。在本文中，我们解决的问题，从一个单一的图像雨条纹去除制定它作为一个层分解问题，与雨条纹层叠加在包含真实场景内容的背景层。现有的分解方法，解决这个问题，采用稀疏字典学习方法或施加一个低秩结构的雨条纹的外观。虽然这些方法可以提高整体可见度，但它们的性能通常不能令人满意，因为它们倾向于过度平滑背景图像或生成仍然包含明显雨条纹的图像。为了解决这些问题，我们提出了一种方法，规定先验的背景和雨条纹层。这些先验是基于在小块上学习的高斯混合模型，这些小块可以适应各种背景外观以及雨条纹的外观。此外，我们引入了结构残留恢复步骤，以进一步分离背景残留，提高分解质量。定量评估表明，我们的方法优于现有的方法的大幅度。我们概述了我们的方法，并证明了其有效性在以前的工作上的一些例子。
Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision and image analysis systems. Rain streak removal algorithms try to recover a rain streak free background scene. In this paper, we address the problem of rain streak removal from a single image by formulating it as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either sparse dictionary learning methods or impose a low rank structure on the appearance of the rain streaks. While these methods can improve the overall visibility, their performance can often be unsatisfactory, for they tend to either over-smooth the background images or generate -images that still contain noticeable rain streaks. To address the problems, we propose a method that imposes priors for both the background and rain streak layers. These priors are based on Gaussian mixture models learned on small patches that can accommodate a variety of background appearances as well as the appearance of the rain streaks. Moreover, we introduce a structure residue recovery step to further separate the background residues and improve the decomposition quality. Quantitative evaluation shows our method outperforms existing methods by a large margin. We overview our method and demonstrate its effectiveness over prior work on a number of examples.