Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement
Guoxi Huang, Qirui Yang, Ruirui Lin, Zipeng Qi, David Bull, Nantheera Anantrasirichai
Abstract
In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions. This naturally results in a one-to-many mapping problem. To address this, we propose a Bayesian Enhancement Model (BEM) that incorporates Bayesian Neural Networks (BNNs) to capture data uncertainty and produce diverse outputs. To enable fast inference, we introduce a BNN-DNN framework: a BNN is first employed to model the one-to-many mapping in a low-dimensional space, followed by a Deterministic Neural Network (DNN) that refines fine-grained image details. Extensive experiments on multiple low-light and underwater image enhancement benchmarks demonstrate the effectiveness of our method.
BibTeX
@inproceedings{aaai2026_bayesianneuralne,
title = {Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement},
author = {Guoxi Huang and Qirui Yang and Ruirui Lin and Zipeng Qi and David Bull and Nantheera Anantrasirichai},
booktitle = {AAAI 2026},
year = {2026}
}