Dehazing with Recovery Level Map: Suppressing Over-Enhancement and Residual Haze

Kentaro Iwamoto, Hiromi Yoshida, Y. Iiguni
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Abstract

Haze degrades contrast and visibility of images, thus it causes bad visibility or poor accuracy in computer vision applications. There are many dehazing methods: prior-based and data-driven methods. Prior-based methods tend to cause over-enhancement such as visual artifacts in the white regions. Data-driven methods cannot sometimes remove haze in the foreground completely. In this paper, we propose a method to suppress both over-enhancement and residual haze based on the dark channel prior (DCP). We use the clarity map as a texture feature and define the recovery level map that determines the amount of dehazing level. We use both the DCP and the recovery level map to estimate the scene transmission. As a result, our method suppresses both over-enhancement and residual haze compared with state-of-the-art dehazing methods.
用恢复等级图去雾:抑制过度增强和残余雾霾
雾霾会降低图像的对比度和可见度,从而导致计算机视觉应用中的可见度差或精度差。除雾方法有多种:基于先验的方法和数据驱动的方法。基于先验的方法往往会导致过度增强,如白色区域的视觉伪影。数据驱动的方法有时不能完全去除前景中的阴霾。在本文中,我们提出了一种基于暗通道先验(DCP)的抑制过度增强和残余雾霾的方法。我们使用清晰度图作为纹理特征,并定义恢复级别图,以确定去雾级别的数量。我们同时使用DCP和恢复级别图来估计场景传输。因此,与最先进的除雾方法相比,我们的方法抑制了过度增强和残余雾霾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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