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
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.
This research / project is supported by the A*STAR - AI3 HTPO Seed Fund (AHSF)
Grant Reference no. : C211118005