Results 21 to 30 of about 750,933 (209)
Structure-Preserving Image Super-Resolution [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Accepted by T-PAMI. Journal version of arXiv:2003.13081 (CVPR 2020)
Cheng Ma +3 more
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Enhanced Deep Residual Networks for Single Image Super-Resolution [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Bee Lim +4 more
semanticscholar +1 more source
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2023 Owe to the powerful generative priors, the pretrained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem.
Rongyuan Wu +5 more
semanticscholar +1 more source
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
semanticscholar +1 more source
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting [PDF]
Neural Information Processing Systems, 2023 Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps.
Zongsheng Yue +2 more
semanticscholar +1 more source
Parallel super-resolution imaging [PDF]
Nature Methods, 2013 Massive parallelization of scanning-based super-resolution imaging allows fast imaging of large fields of view.
Yew, Elijah Y S +2 more
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Image Super-Resolution Using Deep Convolutional Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images.
Chao Dong +3 more
semanticscholar +1 more source
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization [PDF]
European Conference on Computer Vision, 2023 Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stable diffusion models provide a potential solution to the challenging ...
Tao Yang +3 more
semanticscholar +1 more source
Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
semanticscholar +1 more source
Guided filter-based multi-scale super-resolution reconstruction
CAAI Transactions on Intelligence Technology, 2020 The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network.
Xiaomei Feng +3 more
doaj +1 more source