基于物理建模和改进图像可视性恢复的雾霾模拟

Dongwan Ryoo, Kyung-Kyu Kang, Changjoon Park
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引用次数: 0

摘要

本文基于物理模型设计雾霾,提出了一种有效估计雾图像透射量的去霾方法。他[9]提出了一种基于暗通道先验(dark channel prior, DCP)的单幅图像可见性恢复方法。在众多算法中,DCP算法在各种雾图像中都有很好的表现。但是这种方法需要进行消光处理,需要进行大量的计算来细化传输量的块类型。在本文中,我们提出了一种改进的图像可见性恢复算法,该算法使用基于边缘强度信息的混合型多滤波器估计透射量,以更准确地去除边界处的雾霾。通过实验表明,该算法在不使用消光处理的情况下可以有效地消除雾霾。特别是,该算法在边界区域更有效。
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Haze Simulation Based on a Physical Modeling and Improved Image Visibility Restoration
In this paper, we design hazing based on a physical modeling and propose a method to dehaze by efficiently estimating the transmission amount from fog images. He [9] proposed a single image visibility restoration method using dark channel prior (DCP). Among many algorithms, this DCP algorithm is known to have good performance in various fog images. However, this method requires matting processing, which requires a large amount of computation to refine the block type of the transmission amount. In this paper, we propose an improved image visibility restoration algorithm that estimates the transmission amount using a hybrid-type multi-filter based on edge intensity information to more accurately remove haze at the boundary. Through the experiment of a proposed algorithm, we show that haze is effectively eliminated without using matting processing. In particular, the algorithm is more effective at the boundary region.
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