Image correction methods in low-visibility conditions
DOI: 10.21293/1818-0442-2025-28-3-81-88
Abstract: Image restoration in low-visibility conditions is an important issue. Numerous methods have been proposed to address it. However, there is no universal solution equally effective for all shooting conditions. In this work, we compare CLAHE, Reti-nex, the Dark Channel Prior (DCP), and the deep learning model DehazeNet. The BRISQUE no-reference metric was used to evaluate the quality of restored images. Tests on real-world data show that the Dark Channel Prior provides the best metric values among the approaches considered, confirming its versatility and reliability for practical computer vision applica-tions in fog and haze.
Keywords: fog, computer vision, image correction, deep learning
Funding: The research was carried out within the framework of the state assignment of the Institute of Atmospheric Optics SB RAS.
For citation:
Elizarov A. I., Shaleev A. V. Image correction methods in low-visibility conditions. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 3, pp. 81–88. DOI: 10.21293/1818-0442-2025-28-3-81-88
Authors and copyright holders:
- Elizarov A. I. , V.E. Zuev Institute of Atmospheric Optics SB RAS (Tomsk, Russia)
- Shaleev A. V. , V.E. Zuev Institute of Atmospheric Optics SB RAS (Tomsk, Russia), National Research Tomsk State University (Tomsk, Russia)
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