实现雾霾去除算法,增强弱光图像

K. Maheswari, Kadapa R. Charan
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引用次数: 0

摘要

该图像是在有雾的大气条件下拍摄的,导致模糊,视觉上的能见度下降;它模糊了图像质量。而不是产生清晰的图像,基于像素的指标不能保证。更新后的图像用作计算机视觉的输入,用于分割等低级任务。为了改进这一点,它引入了一种新的图像去雾化方法,即端到端方法,以保持生成图像的视觉质量。因此,进一步探索利用网络与U-Net进行语义分割方法的可能性。U-Net将在这个模型中建立和使用,以进一步提高输出的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of haze removal algorithm to enhance low light images
The image is captured in foggy atmospheric conditions, resulting in hazy, visually degraded visibility; it obscures image quality. Instead of producing clear images, pixel-based metrics are not guaranteed. This updated image is used as input in computer vision for low-level tasks like segmentation. To improve this, it introduces a new approach to de-hazing an image, the end-to-end approach, to keep the visual quality of the generated images. So, it takes one step further to explore the possibility of using the network to perform a semantic segmentation method with U-Net. U-Net will be built and used in this model to improve the quality of the output even more.
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