Single Image Dehazing Using Non-local Total Generalized Variation

Renjie He, Xiucai Huang
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引用次数: 1

Abstract

Single image dehazing has been a challenging problem due to its ill-posed nature. In this paper, a novel single image dehazing approach is proposed to accurately model the transmission map and suppress artifacts in the recovered haze-free image. Firstly, a coarse transmission is estimated using the patch based haze-line model. After that, a non-local Total Generalized Variation regularization is introduced to refine the transmission while preserving the local smoothness property and depth discontinuities. In addition, a regularized optimization is proposed for recovering the scene radiance without bringing artifacts boosting. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results indicate that the proposed method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.
基于非局部全广义变分的单幅图像去雾
单图像除雾由于其病态性一直是一个具有挑战性的问题。本文提出了一种新的单幅图像去雾方法,以精确地建模传输图并抑制恢复后无雾图像中的伪影。首先,利用基于贴片的雾线模型估计粗透射率。然后,引入非局部全广义变分正则化来改进传输,同时保持局部平滑性和深度不连续性。在此基础上,提出了一种正则化优化方法,在不引入伪影增强的情况下恢复场景亮度。定量和定性实验结果表明,与现有的除雾方法相比,该方法能够获得准确的透射图和视觉上可信的除雾图像。
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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