AIM-MEF: Multi-exposure image fusion based on adaptive information mining in both spatial and frequency domains

Abstract

Multi-Exposure Image Fusion (MEF) is a simple and effective solution to obtain high dynamic range images by integrating important information from a series of low dynamic range source images with different exposure levels. Despite promising results have been achieved, two issues remain unresolved in existing deep learning-based MEF methods. One is that the information in the frequency domain is underutilized, and the other is that the useful information of hard-to-learn pixels is not fully exploited. To address the above issues, we propose AIM-MEF: a multi-exposure image fusion framework based on adaptive information mining in both spatial and frequency domains. Concretely, we propose an image fusion loss in the frequency domain to mine the frequency domain information for the first time in the MEF field. In order to adaptively exploit the hard-to-learn but useful information in the source images, we propose a hard information mining strategy in both the spatial and the frequency domains, and perform information mining in pixel and frequency levels of the source images. In addition, we propose an adaptive fusion strategy based on information abundance to perform information mining at the image level. We compared AIM-MEF to 10 traditional and deep learning-based methods on two public datasets with eight objective metrics, and AIM-MEF outperformed these baselines in both subjective and objective evaluations. Codes will be publicly available.