Deep Network for Joint Multi-exposure Fusion and Image Deblur

ZHANG Mei; ZHAO Kangwei; ZHU Jinhui

doi:10.11999/JEIT240113

Volume 46 Issue 11

Nov. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(11): 4219-4228

ZHANG Mei, ZHAO Kangwei, ZHU Jinhui. Deep Network for Joint Multi-exposure Fusion and Image Deblur[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4219-4228. doi: 10.11999/JEIT240113

Citation:

ZHANG Mei, ZHAO Kangwei, ZHU Jinhui. Deep Network for Joint Multi-exposure Fusion and Image Deblur[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4219-4228. doi: 10.11999/JEIT240113

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Deep Network for Joint Multi-exposure Fusion and Image Deblur

doi: 10.11999/JEIT240113

ZHANG Mei^{1, 2},
ZHAO Kangwei¹,
ZHU Jinhui^{3, 4
,
,}

1.
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
2.
Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou 510641, China
3.
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
4.
Key Laboratory of Big Data and Intelligent Robotics, Ministry of Education, South China University of Technology, Guangzhou 510641, China

Funds: The National Natural Science Foundation of China (62071184)

Received Date: 2024-02-28
Rev Recd Date: 2024-10-08

Available Online: 2024-10-12

Publish Date: 2024-11-10

Abstract

Abstract

Multi-exposure image fusion is used to enhance the dynamic range of images, resulting in higher-quality outputs. However, for blurred long-exposure images captured in fast-motion scenes, such as autonomous driving, the image quality achieved by directly fusing them with low-exposure images using generalized fusion methods is often suboptimal. Currently, end-to-end fusion methods for combining long and short exposure images with motion blur are lacking. To address this issue, a Deblur Fusion Network (DF-Net) is proposed to solve the problem of fusing long and short exposure images with motion blur in an end-to-end manner. A residual module combined with wavelet transform is proposed for constructing the encoder and decoder, where a single encoder is designed for the feature extraction of short exposure images, a multilevel structure based on encoder and decoder is built for feature extraction of long exposure images with blurring, a residual mean excitation fusion module is designed for the fusion of the long and short exposure features, and finally the image is reconstructed by the decoder. Due to the lack of a benchmark dataset, a multi-exposure fusion dataset with motion blur based on the dataset SICE is created for model training and testing. Finally, the designed model and method are experimentally compared with other state-of-the-art step-by-step optimization methods for image deblurring and multi-exposure fusion from both qualitative and quantitative perspectives to verify the superiority of the model and method in this paper for multi-exposure image fusion with motion blur. The validation is also conducted on a multi-exposure dataset acquired from a moving vehicle, and the effectiveness of the proposed method in solving practical problems is demonstrated by the results.
- Multi-exposure image fusion,
- Image deblurring,
- Wavelet transform,
- Feature fusion

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References(18)

References

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