Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
Year: 2024
Type: article
Abstract: Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple 1×1 DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map.
Source: Applied Sciences
DOI: 10.3390/app14177978
Institution: Yonsei University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4402313832