Inhomogeneous illumination image enhancement under ex-tremely low visibility condition

TL;DR

This paper presents a novel adaptive filtering method using SDIF and MHE to enhance images captured in extremely low visibility foggy conditions, significantly improving signal clarity and contrast.

Contribution

The paper introduces a new adaptive filtering approach combining SDIF and MHE for foggy image enhancement under inhomogeneous illumination, outperforming existing methods.

Findings

Significant enhancement of signal clarity in foggy images.

Outperforms existing image enhancement techniques in low visibility.

Effective in both controlled fog chamber and outdoor environments.

Abstract

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition obscured, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments, and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.