结合Retinex算法和大气散射模型的夜间除雾模型

Hang Yu, Chenyang Li, Zhiheng Liu, Yuru Guo, Zichuan Xie, Suiping Zhou
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引用次数: 1

摘要

为了解决直接使用大气散射模型去雾后对比度低、细节信息丢失严重的问题。本文提出了一种结合Retinex和大气散射模型的夜间图像去雾模型。这个模型结合了两种算法的优点。Retinex可以增强图像的对比度和图像的详细信息。大气散射模型能够清晰地描述雾霾图的成像原理,从根源上还原无雾霾图像。在定性和定量实验中,比较了公共数据集上的四种方法。该算法处理的平均图像信息熵为6.995,平均峰值信噪比为19.340,平均结构相似度为0.766。通过与其他四种算法的对比测试结果表明,本文算法能够有效去除夜间图像中的雾霾,恢复图像的详细信息,促进算法的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Nighttime Dehazing Model Integrating Retinex Algorithm and Atmospheric Scattering Model
To solve the problem of low contrast and serious loss of detail information after dehazing directly using the atmospheric scattering model. This paper proposes a novel night image defogging model that combines the Retinex and the atmospheric scattering models. This model incorporates the best of both algorithms. Retinex can enhance the contrast of the image and the detailed information of the image. The atmospheric scattering model can clearly describe the imaging principle of the haze map, and restore the haze-free image from the root. In qualitative and quantitative experiments, this paper compares four methods on public datasets. This algorithm’s average image information entropy processed by this algorithm is 6.995, the average peak signal-to-noise ratio is 19.340, and the average structural similarity is 0.766. Compared with the other four algorithms, the comparative test results show that the algorithm in this paper can effectively remove the haze in the nighttime image, restore the detailed information of the image, and promote the progress of the algorithm.
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