FDD-MEF: Feature-Decomposition-Based Deep Multi-Exposure Fusion

Abstract

Multi-exposure image fusion is an effective algorithm for fusing differently exposed low dynamic range (LDR) images to a high dynamic range (HDR) images. In this study, a novel network architecture for multi-exposure image fusion (MEF) based on feature decomposition is proposed. The conventional MEF methods are weak for restoring detail and color, and they suffer from visual artifacts. To overcome these challenges, a feature of each LDR image is decomposed to the common and residual components at a feature level. Then, fusion is performed on the residual domain. It was found through diverse experiments that the proposed network could improve the MEF performance in three aspects; detail restoration in bright and dark regions, reduction of halo artifacts, and natural color restoration. In addition, an attempt was made to find the underlying principles of feature-decomposition-based MEF by visualizing the features through RGB channels.

Keywords

• Deep multi-exposure image fusion
• feature decomposition
• detail restoration
• color restoration
• halo artifact reduction