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)