ACE-MEF: Adaptive Clarity Evaluation-Guided Network With Illumination Correction for Multi-Exposure Image Fusion

Abstract

For a natural scene with nonuniform environment light, the captured visible images are always under- or over-exposed because of the limited dynamic range of digital imaging devices. Multi-exposure image fusion (MEF) is a mainstream and effective solution. For a local region that has friendly visual effect in one exposure setting but extremely bad-exposed in another, most existing MEF methods have the ability to transfer the scene detail information to the fused images. However, they will be affected by the over-high or -low light inevitably thus resulting in local visibility reduction. To address this issue, we propose an adaptive clarity evaluation-guided network with illumination correction for MEF in a coarse-to-fine manner, which is termed as ACE-MEF. To be specific, our ACE-MEF is mainly composed of two modules: clarity preservation network (CPN) and illumination adjustment network (IAN). Based on the adaptive clarity evaluation, CPN could be trained to coarsely preserve the environment light and texture details of the clearer regions in source images. Therefore, the need for labeled reference images that are time-consuming to obtain could be mitigated. By measuring the parameter maps of gamma function, IAN is able to refine and correct the local bad-exposed regions so that more details could be further revealed. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively.