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1 deep depth super-resolution : learning depth super-resolution using deep convolutional neural network xibin song, yuchao dai, xueying qin abstract—depth image super-resolution…
deep burst super-resolutioncomputer vision lab, eth zurich, switzerland raw input burst single image sr burst sr (ours) hr reference figure 1. our network generates a super-resolved
self-tuned deep super resolution zhangyang wang† yingzhen yang† zhaowen wang‡ shiyu chang† wei han† jianchao yang♦ and thomas huang† †beckman institute university…
1 super-resolution via deep learning khizar hayat member, ieee. abstract the recent phenomenal interest in convolutional neural networks (cnns) must have made it inevitable…
deep learning for image super-resolution deep learning for image super-resolution chao dong, chen change loy, kaiming he, xiaoou tang presented by prudhvi raj dachapally…
deep learning for image super-resolution deep learning for image super-resolution chao dong, chen change loy, kaiming he, xiaoou tang presented by prudhvi raj dachapally…
super-resolution using constrained deep texture synthesis libin sun∗ brown university james hays† georgia institute of technology abstract hallucinating high frequency…
“zero-shot” super-resolution using deep internal learning assaf shocher∗ nadav cohen† michal irani∗ ∗dept of computer science and applied math the weizmann institute…
1 image super-resolution using deep convolutional networks chao dong, chen change loy, member, ieee, kaiming he, member, ieee, and xiaoou tang, fellow, ieee abstract—we…
deep video super-resolution network using dynamic upsampling filters without explicit motion compensationdynamic upsampling filters without explicit motion compensation younghyun
image super-resolution via deep recursive residual network ying tai∗ 1, jian yang1, and xiaoming liu2 1department of computer science and engineering, nanjing university…
deep video super-resolution network using dynamic upsampling filters without explicit motion compensation younghyun jo seoung wug oh jaeyeon kang seon joo kim yonsei university…
multiscale brain mri super-resolution using deep 3d convolutional networkssubmitted on 17 aug 2019 hal is a multi-disciplinary open access archive for the deposit and dissemination
deep learning enables cross-modality super-resolution in fluorescence microscopy1electrical and computer engineering department, university of california, los angeles, ca,
“zero-shot” super-resolution using deep internal learning assaf shocher∗ nadav cohen† michal irani∗ ∗dept. of computer science and
1. image super-resolution using deep convolutional neural networks (2016) paper reviewed by taegyun jeon dong, chao, et al. "image super-resolution using deep convolutional…
deep learning-based point-scanning super-resolution imagingdeep learning-based point-scanning super-resolution imaging linjing fang1, fred monroe2, sammy weiser novak1, lyndsey
supplementary file: image super-resolution using very deep residual channel attention networks yulun zhang1, kunpeng li1, kai li1, lichen wang1, bineng zhong1, and yun fu1,2…
computational visual media https:doiorg101007s41095-019-0158-8 vol 5 no 4 december 2019 391–401 research article adaptive deep residual network for single image super-resolution…
robust super-resolution via deep learning and tv priors marija vella⋆ colin rickman♯ joão f c mota⋆ ⋆ institute of sensors signals and systems heriot-watt university…