Uncertainty estimation in HDR imaging with Bayesian neural networks

The goal of high dynamic range (HDR) imaging is to estimate potential high-quality images from multi-exposed low dynamic range (LDR) inputs. Intuitively, there exist various possible HDR images corresponding to given LDR inputs, which results in uncertainty in the estimated results. However, most existing HDR imaging methods employ l₁ or l₂ loss only to provide one possible estimation from various possible solutions, which fails to model the uncertainty, and thus lacks high-frequency details. In this work, we design Bayesian neural networks to capture the uncertainty, which can model one-to-many relations and provide various possible solutions. Concretely, we propose a Variational Bayesian Layer by leveraging a hierarchical prior on the network weights and inferring a new joint posterior, which is utilized to model uncertainty in high-frequency details (e.g., textures), and model uncertainty in semantic information (e.g., ghost areas), respectively. By leveraging Bayesian framework, the proposed method can provide various potential high-quality estimations, especially in high-frequency details. Experiments on different datasets show that the proposed method enables sampling possible HDR imaging, and the consensus estimate achieves state-of-the-art quantitative and qualitative results.