A Novel Attention-guided Network for Deep High Dynamic Range Imaging

In natural scenes with multi-exposure image fusion (MEF), high dynamic range (HDR) imaging is often affected by moving objects or misalignments in the scene, resulting in ghosting artifacts in the final imaging results, with the help of optical flow method and deep network architecture. To avoid ghosting artifacts better, we propose a novel attention- guided neural network (ADeepHDR) to produce high-quality ghost-free HDR images. Unlike the previous methods, we use the attention module to guide the process of image merging. The attention module can detect the large motions and the notable parts of the different input features and enhance details in the results. Based on the attention module, we also try different subnetwork variants to make full use of the hierarchical features to get more ideal results. Besides, fractional-oder differential convolution is used in the subnetwork variant to extract more detailed features. The proposed ADeepHDR is an improvement method without optical flows, which can better avoid the ghosting artifacts caused by error optical flow estimation and large motions. We have conducted extensive quantitative and qualitative assessments, and show that the proposed method is superior to the most state-of-the- art approaches.