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|a 10.1109/TIP.2022.3184819
|2 doi
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|a eng
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| 100 |
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|a Guo, Yanhui
|e verfasserin
|4 aut
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| 245 |
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|a Data Acquisition and Preparation for Dual-Reference Deep Learning of Image Super-Resolution
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|c 2022
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|a Text
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 06.07.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The performance of deep learning based image super-resolution (SR) methods depend on how accurately the paired low and high resolution images for training characterize the sampling process of real cameras. Low and high resolution (LR ∼ HR) image pairs synthesized by degradation models (e.g., bicubic downsampling) deviate from those in reality; thus the synthetically-trained DCNN SR models work disappointingly when being applied to real-world images. To address this issue, we propose a novel data acquisition process to shoot a large set of LR ∼ HR image pairs using real cameras. The images are displayed on an ultra-high quality screen and captured at different resolutions. The resulting LR ∼ HR image pairs can be aligned at very high sub-pixel precision by a novel spatial-frequency dual-domain registration method, and hence they provide more appropriate training data for the learning task of super-resolution. Moreover, the captured HR image and the original digital image offer dual references to strengthen supervised learning. Experimental results show that training a super-resolution DCNN by our LR ∼ HR dataset achieves higher image quality than training it by other datasets in the literature. Moreover, the proposed screen-capturing data collection process can be automated; it can be carried out for any target camera with ease and low cost, offering a practical way of tailoring the training of a DCNN SR model separately to each of the given cameras
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|a Journal Article
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| 700 |
1 |
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|a Wu, Xiaolin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shu, Xiao
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 27., Seite 4393-4404
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:31
|g year:2022
|g day:27
|g pages:4393-4404
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|u http://dx.doi.org/10.1109/TIP.2022.3184819
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|d 31
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|b 27
|h 4393-4404
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