Bayesian Enhancement Model (BEM)
AAAI 2026 — Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement
🌟 Overview
Image enhancement is inherently one-to-many — a single degraded image often corresponds to multiple plausible enhanced outputs, especially in low-light and underwater environments.
However, existing deterministic models can only produce one prediction, failing to capture this ambiguity.
BEM (Bayesian Enhancement Model) solves this by:
Modeling uncertainty using a Bayesian Neural Network (BNN)
Generating multiple valid enhancement candidates
Selecting or aggregating candidates via Ranking or Monte Carlo
Refining details using a second-stage DNN
Achieving DNN-level inference speed with BNN-level diversity
This makes BEM the first practical Bayesian solution for large-scale image enhancement tasks.
🔍 Key Features
✓ One-to-Many Enhancement via Bayesian Inference
BEM samples weights from a variational posterior learned by a Bayesian UNet.
Each sample produces a distinct enhancement, capturing the ambiguity of the task
