Deep Gradual Multi-Exposure Fusion via Recurrent Convolutional Network

The performance of multi-exposure image fusion (MEF) has been recently improved with deep learning techniques but there are still a couple of problems to be overcome. In this paper, we propose a novel MEF network based on recurrent neural network (RNN). Multi-exposure images have different useful in...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.1-1
Main Authors: Ryu, Je-Ho, Kim, Jong-Han, Kim, Jong-Ok
Format: Article
Language:English
Subjects:
Brightness
Computer networks
Computer vision
Deep learning
Dilated convolution filter
Exposure
Feature extraction
Fuses
Gradual fusion
Image fusion
Image processing
Image reconstruction
Image restoration
Multi-exposure image fusion
Recurrent convolutional network
Recurrent neural networks
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Summary:The performance of multi-exposure image fusion (MEF) has been recently improved with deep learning techniques but there are still a couple of problems to be overcome. In this paper, we propose a novel MEF network based on recurrent neural network (RNN). Multi-exposure images have different useful information depending on their exposure levels, and in order to fuse them complementarily, we first extract the local detail and global context features of input source images, and both features are separately combined. A weight map is learned from the local features for effectively fusing according to the importance of each source image. Adopting RNN as a backbone network enables gradual fusion, where more inputs result in further improvement of the fusion gradually. Also, information can be transferred to the deeper level of the network. Experimental results show that the proposed method achieves the reduction of fusion artifacts and improves detail restoration performance, compared to conventional methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3122540