Adaptive Image-Defogging Algorithm Based on Bright-Field Region Detection

• Published in: Photonics Vol. 11; no. 8; p. 718
• Main Authors: Wang, Yue, Yue, Fengying, Duan, Jiaxin, Zhang, Haifeng, Song, Xiaodong, Dong, Jiawei, Zeng, Jiaxin, Cui, Sidong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Adaptive algorithms; Algorithms; bright-field region segmentation; Dark adaptation; dark-channel prior; Deep learning; Fog; Halo effect; image defogging; Image enhancement; Image filters; Image processing; Image quality; Light; Medical imaging; Methods; Military applications; Military technology; Parameter estimation; Remote monitoring; Remote sensing; Satellite imagery; Traffic accidents & safety; Traffic surveillance; transmission; Transmittance; Visual effects
• DOI: 10.3390/photonics11080718
• ISSN: 2304-6732

Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with the dark-channel prior being the most frequently employed. However, halo and bright-field color distortion issues persist. To further improve image quality, an adaptive image-defogging algorithm based on bright-field region detection is proposed in this paper. Modifying the dark-channel image improves the abrupt changes in gray value in the traditional dark-channel image. By setting the first and second lower limits of transmittance and introducing an adaptive correction factor to adjust the transmittance of the bright-field region, the limitations of the dark-channel prior in extensive ranges and high-brightness areas can be significantly alleviated. In addition, a guide filter is utilized to enhance the initial transmittance image, preserving the details of the defogged image. The results of the experiment demonstrate that the algorithm presented in this paper effectively addresses the mentioned issues and has shown outstanding performance in both objective evaluation and subjective visual effects.