Neural Augmented Exposure Interpolation for Two Large-Exposure-Ratio Images

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Neural Augmented Exposure Interpolation for Two Large-Exposure-Ratio Images
Title:
Neural Augmented Exposure Interpolation for Two Large-Exposure-Ratio Images
Journal Title:
IEEE Transactions on Consumer Electronics
Publication Date:
17 October 2022
Citation:
Zheng, C., Jia, W., Wu, S., & Li, Z. (2023). Neural Augmented Exposure Interpolation for Two Large-Exposure-Ratio Images. IEEE Transactions on Consumer Electronics, 69(1), 87–97. https://doi.org/10.1109/tce.2022.3214382
Abstract:
Brightness order reversal could happen among shadow regions in a bright image and high-light regions in a dark image if two large-exposure-ratio images are fused directly by using existing multi-scale exposure fusion (MEF) algorithms. This problem can be addressed effectively via exposure interpolation. In this paper, a novel exposure interpolation algorithm is introduced by combining model-based and data-driven approaches to form a neural augmented interpolation framework. An image with a medium-exposure is initially interpolated by using intensity mapping functions (IMFs), and then refined via a novel exposedness aware network (EA-Net). Experimental results indicate that the model-based approach is improved by the data-driven approach, and the data-driven approach is benefited from the model-based method for fast convergence speed and learning with few training samples. The explainability of both the new EA-Net and the proposed framework is improved via such a neural augmentation.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AI3 HTPO Seed Fund (AHSF)
Grant Reference no. : C211118005
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0098-3063
1558-4127
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