Inhomogeneous illumination image enhancement under ex-tremely low visibility condition
This work addresses image enhancement for deep fog imaging applications, offering improvements for tasks like object detection, but it appears incremental as it builds on existing neural network-based approaches.
The paper tackled the problem of image enhancement in dense fog with inhomogeneous illumination, which hinders object detection and recognition, and demonstrated that their method significantly enhances signal clarity and outperforms existing techniques under extremely low visibility conditions.
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.