Polarization-Blind Image Dehazing Algorithm Based on Joint Polarization Model in Turbid Media
Year: 2025
Type: article
Abstract: To address the issue of reduced image contrast and visibility caused by turbid media, such as dense fog, this paper proposes a novel polarization-based single-image dehazing model. The model introduces a first-of-its-kind nonlinear joint polarization model for airlight and target light. This model is established within a Cartesian coordinate system, abstracted as an analytical geometric model. Leveraging the structural similarity principle in images, boundary constraints are applied to enhance the accuracy of target light estimation. Finally, image dehazing and enhancement are achieved using the atmospheric scattering model. Experimental results demonstrate that the proposed algorithm does not rely on dataset training, maintains the highest structural consistency, and achieves superior image restoration across various scenarios, producing results that most closely resemble natural observation.
Source: Applied Sciences
DOI: 10.3390/app152010957
Authors: Zhen Wang, Zhenduo Zhang, Rui Ma, Xueying Cao
Institution: Dalian Maritime 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: $2,896
OpenAlex ID: W4415129206