单幅图像去雾方法的不同雾霾图像条件

Noor Asma Husain, M. Rahim, Huma Chaudhry
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引用次数: 0

摘要

大气中的灰尘、薄雾、薄雾和烟雾通常会降低光和吸收的图像。这些效果具有可视性差、亮度变暗、对比度低和色彩失真的特点。因此,恢复退化的图像是困难的,特别是在朦胧的条件下。该图像去雾方法侧重于在不造成数据丢失的情况下,在保持图像颜色的同时提高图像细节的可见性。许多图像去雾方法实现了去除雾霾的目标,同时也解决了其他问题,如过饱和度,色彩失真和光晕伪影。然而,由于雾霾程度的限制,这些方法都是无效的。需要大量的各种雾霾级别的数据来证明图像去雾方法在去除所有雾霾级别的雾霾和获得图像质量方面的效率。本文将动态散射系数引入到消雾算法中,以确定不同雾霾条件下的能见度范围。这些提出的方法在图像质量测量结果方面改进了当前最先进的除雾方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Different Haze Image Conditions for Single Image Dehazing Method
The dust, mist, haze, and smokiness of the atmosphere typically degrade images from the light and absorption. These effects have poor visibility, dimmed luminosity, low contrast, and distortion of colour. As a result, restoring a degraded image is difficult, especially in hazy conditions. The image dehazing method focuses on improving the visibility of image details while preserving image colours without causing data loss. Many image dehazing methods achieve the goal of removing haze while also addressing other issues such as oversaturation, colour distortion, and halo artefacts. However, the limitation of haze level rendered these approaches ineffective. A volume of various haze level data is required to demonstrate the efficiency of the image dehazing method in removing haze at all haze levels and obtaining the image's quality. This paper introduced a dynamic scattering coefficient to the dehazing algorithm for determining an applicable visibility range for different haze conditions. These proposed methods improve on the current state-of-the-art dehazing method in terms of image quality measurement results.
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