Abstract
Imaging through fog significantly impacts fields such as object detection and recognition. In conditions of extremely low visibility, essential image information can be obscured, rendering standard extraction methods ineffective. Traditional digital processing techniques, such as histogram stretching, aim to mitigate fog effects by enhancing object light contrast diminished by atmospheric scattering. However, these methods often experience reduce effectiveness under inhomogeneous illumination. This paper introduces a novel approach that adaptively filters background illumination under extremely low visibility and preserve only the essential signal information. Additionally, we employ a visual optimization strategy based on image gradients to eliminate grayscale banding. Finally, the image is transformed to achieve high contrast and maintain fidelity to the original information through maximum histogram equalization. Our proposed method significantly enhances signal clarity in conditions of extremely low visibility and outperforms existing algorithms.
Abstract (translated)
雾中的图像成像对诸如目标检测和识别等领域产生了显著影响。在极度低能见度的情况下，关键图像信息可能会被遮挡，导致标准提取方法变得无效。传统的数字处理技术，如直方图伸缩，试图通过增强物体光线对比度来减轻雾的影响。然而，这些方法在非均匀光照条件下往往效果减弱。本文提出了一种新方法，可以在极度低能见度条件下自适应地过滤背景光照，并仅保留关键信号信息。此外，我们还采用基于图像梯度的视觉优化策略来消除灰度带。最后，通过最大直方图均衡，将图像变换以实现高对比度并保持原始信息的完整性。与现有算法相比，我们提出的方法在极度低能见度条件下显著增强了信号清晰度，并表现出色。
URL
https://arxiv.org/abs/2404.17503
