Saliency-based dark channel prior model for single image haze removal
Saliency-based dark channel prior model for single image haze removal
复制标题
基于显着性的暗通道先验模型用于单图像去雾
DOI：
10.1049/iet-ipr.2017.0959
复制
发表时间：
2018
影响因子：
2.3
通讯作者：
Wang Xiaohan
中科院分区：
文献类型：
--
作者：
Zhang Libao;Wang Shiyi;Wang Xiaohan
被雾霾降级的图像通常对比度低，颜色真实，因此会对目标跟踪、人脸识别和智能监控等应用产生不良影响。因此，去噪的目的是恢复图像的对比度而不失真颜色。暗通道先验算法(DCP)因其简单有效而被广泛应用于雾霾去除领域。然而，当面对明亮的白色物体时，DCP高估了雾霾的真实值，从而导致颜色失真。在这项研究中，作者提出了一种结合显著检测和DCP的去混模型，以获得颜色失真较小的恢复图像。主要贡献有三个。首先，提出了一种新的基于超像素亮度对比度的显著检测方法，用于提取模糊图像中的明亮白色目标。这些对象不用于估计暗通道图像中的大气光和透射率。其次，为场景亮度设置自适应上限，以防止某些区域过亮。第三，提出了一个评价颜色恢复效果的量化指标--颜色差异距离。实验结果表明，与竞争模型相比，他们提出的模型产生的颜色失真更小，并且具有更好的综合性能。
Images degraded by haze usually have low contrast and fide colours, and thus have bad effects on applications such as object tracking, face recognition, and intelligent surveillance. So the purpose of dehazing is to recover the image contrast without colour distortion. The dark channel prior (DCP) is widely used in the field of haze removal because of its simplicity and effectiveness. However, when faced with bright white objects, DCP overestimates the haze from its true value and thus causes colour distortion. In this study, the authors propose a dehazing model combining saliency detection with DCP to obtain recovered images with little colour distortion. There are three main contributions. First, they introduce a novel saliency detection method, focusing on superpixel intensity contrast, to extract bright white objects in the hazy image. Those objects are not used to estimate the atmospheric light and transmission in the dark channel image. Second, a self-adaptive upper bound is set for the scene radiance to prevent some regions being too bright. Third, they propose a quantitative indicator, colour variance distance, to evaluate the colour restoration. Experimental results show that their proposed model generates less colour distortion and has better comprehensive performance than competing models.