@inproceedings{9aabd7bc9d0a45bbbba346bfec06dd3f,
title = "Variance-Driven U-Net Training and Chroma-Scale-Based Multi-Exposure Image Fusion",
abstract = "We propose a multi-exposure fusion framework that integrates variance-driven U-Net training which suppresses data imbalance and noise amplification. To further enhance visual fidelity, we introduce a chroma-map strategy that redistributes the ab channel chroma scale. Combining variance-driven learning with chroma-guided correction, the proposed method produces high-quality HDR synthesis with improved detail and faithful color reproduction while maintaining computational efficiency. Compared with Conv MSR, DSIFT\_EF, DEM, and DBM, the results demonstrate improvements in preservation of detail.",
author = "Son, \{Chang Woo\} and Go, \{Young Ho\} and Lee, \{Seung Hwan\} and Lee, \{Sung Hak\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 17th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025 ; Conference date: 22-10-2025 Through 24-10-2025",
year = "2025",
doi = "10.1109/APSIPAASC65261.2025.11249185",
language = "English",
series = "2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2608--2609",
booktitle = "2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025",
address = "United States",
}