Inhomogeneous illuminated image enhancement under extremely low visibility condition
arxiv(2024)
摘要
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.
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