Multi-Exposure Fusion with CNN Features

Multi-exposure fusion (MEF) is a widely used approach to high dynamic range imaging. The selection of features for fusion weight calculation is important to the performance of MEF. In this paper, we investigate the effectiveness of convolutional neural network (CNN) features for MEF. Considering the fact that there are no ground-truth images in MEF to train an end-to-end CNN, we adopt the pre-trained networks in other tasks to extract the feature. Both the selection of network and the selection of convolution layer are studied. With the extracted CNN feature map, we compute the local visibility and consistency maps to determine the weight map for MEF. The proposed method works well for both static and dynamic scenes. It exhibits competitive quantitative measures, and presents perceptually pleasing MEF outputs with little halo effects.