Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes

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Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes
Title:
Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes
Journal Title:
IEEE Transactions on Instrumentation and Measurement
Publication Date:
14 August 2023
Citation:
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).
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:
1557-9662
0018-9456
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