Neural Augmented Exposure Interpolation for HDR Imaging

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Neural Augmented Exposure Interpolation for HDR Imaging
Title:
Neural Augmented Exposure Interpolation for HDR Imaging
Journal Title:
2023 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
11 September 2023
Citation:
Li, Z., Zheng, C., Zheng, J., & Wu, S. (2023). Neural Augmented Exposure Interpolation for HDR Imaging. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10222208
Abstract:
Brightness order reversal usually appears when two large-exposure ratio images of a high dynamic range scene are directly fused together by an existing multi-scale exposure fusion algorithm. To address the problem, a novel neural augmented framework is introduced to interpolate an image with the medium exposure by integrating physics-driven and data-driven approaches. The physics driven method infers high-frequency information while the data driven approach learns remaining information for the interpolated image. The interpolated image and two large-exposure-ratio images are fused together. Experimental results show that the proposed framework can indeed solve the brightness order reversal problem for the fusion of two large-exposure-ratio images.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: I2R
Grant Reference no. : n.a

This work was supported in part by Nature Science Foundation of Hubei Province, China (Grant No. 2022CFB676)
Description:
© 2023 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:
1558-0792
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