用贝叶斯网络让一张暗图生成多种合理增强结果。
Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement
- 先用贝叶斯网络在低维空间建模多解映射关系。
- 再用确定性网络修复细节，推理速度更快。
- 适合需要多样输出的图像增强任务。
在低光和水下图像增强任务中，由于摄影条件动态变化，一张退化图像可能对应多个合理的增强目标，天然形成一对多映射问题。为此，我们提出贝叶斯增强模型（BEM），引入贝叶斯神经网络（BNN）以捕捉数据不确定性，生成多样化输出。为实现快速推理，设计BNN-DNN框架：首先用BNN在低维空间建模一对多映射，再由确定性神经网络（DNN）精细化修复图像细节。在多个低光与水下图像增强基准测试上，实验充分验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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