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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/143302

Title:	Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction
Authors:	彭彥璁 Peng, Yan-Tsung Liao, He-Hao;Chen, Ching-Fu
Contributors:	資科系
Keywords:	two-exposure image fusion;gamma correction;high dynamic imaging
Date:	2021-12
Issue Date:	2023-02-06 14:30:36 (UTC+8)
Abstract:	In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.
Relation:	Sensors, Vol.22, No.1, 24
Data Type:	article
DOI 連結:	https://doi.org/10.3390/s22010024
DOI:	10.3390/s22010024
Appears in Collections:	[資訊科學系] 期刊論文

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