Multi-Scale Exposure Fusion via Content Adaptive Edge-Preserving Smoothing Pyramids

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Multi-Scale Exposure Fusion via Content Adaptive Edge-Preserving Smoothing Pyramids
Title:
Multi-Scale Exposure Fusion via Content Adaptive Edge-Preserving Smoothing Pyramids
Journal Title:
IEEE Transactions on Consumer Electronics
Publication Date:
23 August 2022
Citation:
Jia, W., Song, Z., & Li, Z. (2022). Multi-Scale Exposure Fusion via Content Adaptive Edge-Preserving Smoothing Pyramids. IEEE Transactions on Consumer Electronics, 68(4), 317–326. https://doi.org/10.1109/tce.2022.3200707
Abstract:
Multi-scale exposure fusion (MEF) is an efficient way to fuse differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into an information enriched LDR image. In this paper, a new MEF algorithm is proposed to merge the differently exposed LDR images by introducing novel content adaptive edge-preserving smoothing (CAS) pyramids for the weight maps of all the LDR images. With the proposed CAS pyramids, details in the brightest and darkest regions of the HDR scene are preserved better than existing MEF algorithms on top of the Gaussian pyramids and edge-preserving smoothing pyramids. Comparisons experimentally demonstrate the effectiveness of the proposed algorithm to nine state-of-theart MEF algorithms from both subjective and objective points of view regardless the image sizes
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 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:
1558-4127
0098-3063
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