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• W4366831419 startingPage "116984" @default.
• W4366831419 abstract "Video super-resolution (VSR) reconstruction technique aims to improve the spatiotemporal resolution of consecutive low resolution (LR) frames. Most of previous methods use the correlation of inter-frame to restore pixels. It is still a challenge to exploit intra-frame and inter-frame correlations to recover high-resolution (HR) frames, especially reconstruction of video in low-light conditions for sharp imaging results has been a bottleneck in current industrial environments. Therefore, this paper proposes a novel VSR network. Specifically, we first generate the hidden information of the current frame by using the appropriate combination of the front and rear frames. Then, a re-fusion block (RFB) is designed, which utilizes the hidden information to re-fuse with corresponding LR frame. After that, we integrate an improved dual attention mechanism (DAM) into network to extract more accurate feature of intra-frame without increasing the number of parameters. The technology has important practical significance in low-light and dim scenes, so we collect some industrial video sequences and make datasets to complete VSR task. Experimental results show that our model significantly outperforms state-of-the-art methods in performance." @default.
• W4366831419 created "2023-04-25" @default.
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• W4366831419 creator A5030439377 @default.
• W4366831419 creator A5047225155 @default.
• W4366831419 creator A5060178337 @default.
• W4366831419 date "2023-07-01" @default.
• W4366831419 modified "2023-09-30" @default.
• W4366831419 title "Dual feature enhanced video super-resolution network based on low-light scenarios" @default.
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• W4366831419 doi "https://doi.org/10.1016/j.image.2023.116984" @default.
• W4366831419 hasPublicationYear "2023" @default.
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