Variance-Driven U-Net Weighted Training and Chroma-Scale-Based Multi-Exposure Image Fusion

Chang-Woo Son, Young-Ho Go, Seung-Hwan Lee and Sung-Hak Lee ()
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Chang-Woo Son: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Young-Ho Go: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Seung-Hwan Lee: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
Sung-Hak Lee: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea

Mathematics, 2025, vol. 13, issue 22, 1-32

Abstract: Multi-exposure image fusion (MEF) aims to generate a well-exposed image by combining multiple photographs captured at different exposure levels. However, deep learning-based approaches are often highly dependent on the quality of the training data, which can lead to inconsistent color reproduction and loss of fine details. To address this issue, this study proposes a variance-driven hybrid MEF framework based on a U-Net architecture, which adaptively balances structural and chromatic information. In the proposed method, the variance of randomly cropped patches is used as a training weight, allowing the model to emphasize structurally informative regions and thereby preserve local details during the fusion process. Furthermore, a fusion strategy based on the geometric color distance, referred to as the Chroma scale, in the LAB color space is applied to preserve the original chroma characteristics of the input images and improve color fidelity. Visual gamma compensation is also employed to maintain perceptual luminance consistency and synthesize a natural fine image with balanced tone and smooth contrast transitions. Experiments conducted on 86 exposure pairs demonstrate that the proposed model achieves superior fusion quality compared with conventional and deep-learning-based methods, obtaining high JNBM (17.91) and HyperIQA (70.37) scores. Overall, the proposed variance-driven U-Net effectively mitigates dataset dependency and color distortion, providing a reliable and computationally efficient solution for robust MEF applications.

Keywords: chroma scale; HDR; multi-exposed image fusion; U-Net (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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