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.

翻译：雾天成像严重影响目标检测与识别等领域。在极低能见度条件下，关键图像信息可能被遮蔽，导致标准提取方法失效。传统数字处理技术（如直方图拉伸）通过增强因大气散射而降低的目标光对比度来减轻雾效，但此类方法在非均匀光照条件下效果往往减弱。本文提出一种新方法，可在极低能见度下自适应滤波背景光照，仅保留关键信号信息；同时，采用基于图像梯度的视觉优化策略消除灰度条带效应；最后，通过最大直方图均衡化将图像转换为高对比度并保持原始信息保真度。所提方法在极低能见度条件下显著提升信号清晰度，且性能优于现有算法。