政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/138325

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题名:	Two Exposure Fusion Using Prior-Aware Generative Adversarial Network
作者:	彭彥璁 Peng, Yan-Tsung Yin, Jia-Li Chen, Bo-Hao
贡献者:	資科系
关键词:	High dynamic range image;exposure fusion;deep learning
日期:	2021-06
上传时间:	2021-12-23 15:41:23 (UTC+8)
摘要:	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.
關聯:	IEEE Transactions on Multimedia, pp.1941-0077
数据类型:	article
DOI 連結:	https://doi.org/10.1109/TMM.2021.3089324
DOI:	10.1109/TMM.2021.3089324
显示于类别:	[資訊科學系] 期刊論文

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