Visibility Enhancement in Low Light Images with Weighted Fusion of Robust Retinex Model and Dark Channel Prior

Sudeep D. Thepade, Akshay Shirbhate
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引用次数: 2

Abstract

Images captured under poor illumination or at night time doesn’t have significant details as compared to images captured under proper lighting conditions. These images, when used for computer vision applications might be the reason for undesirable output. So, these kinds of images are not suitable for observation and analysis is case of any computer vision application. To solve this problem, visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement method have been proposed in the literature. The paper proposes visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement. The validation of proposed method is judged based on entropy. The performance based on the entropy as measure, is evaluated for proposed system and compared with the other existing popular low light image enhancement techniques. For rigorous validation, different weights combinations are explored in the proposed fusion based image enhancement method.
基于鲁棒视网膜模型和暗通道先验加权融合的弱光图像可见性增强
与在适当的照明条件下拍摄的图像相比,在较差的照明条件下或夜间拍摄的图像没有重要的细节。这些图像,当用于计算机视觉应用程序时,可能是不希望输出的原因。因此,这类图像不适合任何计算机视觉应用的情况下进行观察和分析。为了解决这一问题,文献中提出了基于鲁棒视网膜模型加权融合的弱光图像可见性增强方法和基于暗通道先验的增强方法。本文提出了一种基于鲁棒视网膜模型和暗通道先验增强加权融合的弱光图像可见性增强方法。基于熵判断方法的有效性。以熵为度量指标,评价了该系统的性能,并与现有的其他流行的弱光图像增强技术进行了比较。为了更严格的验证,所提出的基于融合的图像增强方法探索了不同的权重组合。
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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