Abstract
Since the existing commercial imaging equipment cannot meet the requirements of high dynamic range, multi-exposure image fusion is an economical and fast method to implement HDR. However, the existing multi-exposure image fusion algorithms have the problems of long fusion time and large data storage. We propose an extreme exposure image fusion method based on deep learning. In this method, two extreme exposure image sequences are sent to the network, channel and spatial attention mechanisms are introduced to automatically learn and optimize the weights, and the optimal fusion weights are output. In addition, the model in this paper adopts real-value training and makes the output closer to the real value through a new custom loss function. Experimental results show that this method is superior to existing methods in both objective and subjective aspects.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
