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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/138325

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/138325

Title:	Two Exposure Fusion Using Prior-Aware Generative Adversarial Network
Authors:	彭彥璁 Peng, Yan-Tsung Yin, Jia-Li Chen, Bo-Hao
Contributors:	資科系
Keywords:	High dynamic range image;exposure fusion;deep learning
Date:	2021-06
Issue Date:	2021-12-23 15:41:23 (UTC+8)
Abstract:	Producing a high dynamic range (HDR) image from two low dynamic range (LDR) images with extreme exposures is challenging due to the lack of well-exposed contents. Existing works either use pixel fusion based on weighted quantization or conduct feature fusion using deep learning techniques. In contrast to these methods, our core idea is to progressively incorporate the pixel domain knowledge of LDR images into the feature fusion process. Specifically, we propose a novel Prior-Aware Generative Adversarial Network (PA-GAN), along with a new dual-level loss for two exposure fusion. The proposed PA-GAN is composed of a content prior guided encoder and a detail prior guided decoder, respectively in charge of content fusion and detail calibration. We further train the network using a dual-level loss that combines the semantic-level loss and pixel-level loss. Extensive qualitative and quantitative evaluations on diverse image datasets demonstrate that our proposed PA-GAN has superior performance than state-of-the-art methods.
Relation:	IEEE Transactions on Multimedia, pp.1941-0077
Data Type:	article
DOI 連結:	https://doi.org/10.1109/TMM.2021.3089324
DOI:	10.1109/TMM.2021.3089324
Appears in Collections:	[資訊科學系] 期刊論文

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