Zheng, C., Ying, W., Wu, S., & Li, Z. (2023). Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes. IEEE Transactions on Instrumentation and Measurement, 72, 1–11. https://doi.org/10.1109/tim.2023.3304675
Abstract:
There are shadow and highlight regions in a lowdynamic-
range (LDR) image which is captured from a highdynamic-
range (HDR) scene. It is an ill-posed problem to restore
the saturated regions of the LDR image. In this article, the
saturated regions of the LDR image are restored by fusing
model-based and data-driven approaches. With such a neural
augmentation, two synthetic LDR images are first generated from
the underlying LDR image via the new model-based approach.
It relaxes the requirement of mapping integers to integers and
improves the modeling accuracy. One is brighter than the input
image to restore the shadow regions and the other is darker
than the input image to restore the highlight regions. Both
synthetic images are then refined via one single exposednessaware
saturation restoration network (EASRN). Finally, the two
synthetic images and the input image are combined together
via an HDR synthesis algorithm or a multiscale exposure
fusion (MEF) algorithm. Experimental results indicate that the
proposed algorithm outperforms existing algorithms in terms
of HDR-VDP-3. The proposed algorithm can be embedded in
any smartphones or digital cameras to produce an informationenriched
LDR image.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Robotics Horizontal Technology Coordinating Office (HTCO)
Grant Reference no. : C221518005
This work was supported in part by Nature Science Foundation of Hubei
Province, China (Grant No. 2022CFB676).