Open AccessAn Efficient Extreme-Exposure Image Fusion Method
Author(s) -
Jiebin Zhang,
Shangyou Zeng,
Ying Wang,
Jinjin Wang,
Hongyang Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2137/1/012061
Subject(s) - image fusion , image (mathematics) , computer science , fusion , artificial intelligence , range (aeronautics) , function (biology) , channel (broadcasting) , computer vision , fusion rules , engineering , telecommunications , philosophy , linguistics , evolutionary biology , biology , aerospace engineering
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.