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

• URL: http://arxiv.org/abs/2501.14265v2
• Date: Thu, 30 Jan 2025 17:19:05 GMT
• Title: Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement
• Authors: Guoxi Huang, Nantheera Anantrasirichai, Fei Ye, Zipeng Qi, RuiRui Lin, Qirui Yang, David Bull,
• Abstract summary: In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images. We propose a Bayesian Enhancement Model (BEM) that incorporates Bayesian Neural Networks (BNNs) to capture data uncertainty and produce diverse outputs. To achieve real-time inference, we introduce a two-stage approach: Stage I employs a BNN to model the one-to-many mappings in the low-dimensional space, while Stage II refines fine-grained image details.
• Score: 13.032601777946944
• License:
• 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, such as variations in illumination. This naturally results in a one-to-many mapping challenge. 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 achieve real-time inference, we introduce a two-stage approach: Stage I employs a BNN to model the one-to-many mappings in the low-dimensional space, while Stage II refines fine-grained image details using a Deterministic Neural Network (DNN). To accelerate BNN training and convergence, we introduce a dynamic Momentum Prior. Extensive experiments on multiple low-light and underwater image enhancement benchmarks demonstrate the superiority of our method over deterministic models.

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