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license: mit
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Bayesian Enhancement Model (BEM)

AAAI 2026 — Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

Paper: [arxiv:2501.14265](https://arxiv.org/abs/2501.14265)

Github: https://github.com/BinCVER/BEM

🌟 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