ALSP+: Fast Scene Recovery via Ambient Light Similarity Prior

The absorption and scattering of light in different turbid media cause images to suffer from poor visibility and contrast, which severely affects the performance of many computer vision tasks. To address this issue, we propose a fast scene recovery method based on the Ambient light similarity prior (ALSP). In this method, the ambient light similarity metric is designed from both magnitude and orientation, which is embedded into the optical imaging model, and the estimation of scene transmission is derived by simplification and approximation. The estimation of the transmission map is very simple, and its time complexity is O(N), where N is the size of the input image. Moreover, we propose a progressive manner to determine the ambient light for both the near and far regions separately, which can effectively improve the brightness and color saturation of the restored image. Experiments performed in different scenes demonstrate that our method outperforms several state-of-the-art competitors in terms of efficiency and scene recovery performance.

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Publication Details

Journal
IEEE Transactions on Image Processing
Published
2025-01-01
DOI
https://doi.org/10.1109/tip.2025.3586514
Citations
11
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
14.04

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ALSP+: Fast Scene Recovery via Ambient Light Similarity Prior

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article

ALSP+: Fast Scene Recovery via Ambient Light Similarity Prior

Zunhui Yi, Jinshi Liu, Ming Lu, Zhipeng Chen, Chaoyang Chen, Lei He
article en
11 citations

Abstract

The absorption and scattering of light in different turbid media cause images to suffer from poor visibility and contrast, which severely affects the performance of many computer vision tasks. To address this issue, we propose a fast scene recovery method based on the Ambient light similarity prior (ALSP). In this method, the ambient light similarity metric is designed from both magnitude and orientation, which is embedded into the optical imaging model, and the estimation of scene transmission is derived by simplification and approximation. The estimation of the transmission map is very simple, and its time complexity is O(N), where N is the size of the input image. Moreover, we propose a progressive manner to determine the ambient light for both the near and far regions separately, which can effectively improve the brightness and color saturation of the restored image. Experiments performed in different scenes demonstrate that our method outperforms several state-of-the-art competitors in terms of efficiency and scene recovery performance.

IEEE Transactions on Image ProcessingVol. 34
Hunan University of Science and Technology (CN), Central South University (CN)
National Natural Science Foundation of China - State Grid Corporation Joint Fund for Smart Grid
Climate action
Openalex Percentile: Top 1%
Image Enhancement Techniques
14.04
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