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Bridging the resolution gap: correlative super-resolution imaging
Nature Reviews Microbiology, 2019 This month’s Under the Lens discusses progress towards bridging the resolution gap in correlative super-resolution light and electron microscopy, and highlights its application for visualizing bacterial ...
Ian M Dobbie
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Image Super-Resolution Via Sparse Representation
IEEE Transactions on Image Processing, 2010 This thesis presents a new approach to single-image super-resolution (SR), based on sparse signal recovery. Research on image statistics suggests that image patches can be well represented as a sparse linear combination of elements from an appropriately ...
Jianchao Yang +3 more
semanticscholar +2 more sources
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Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
Super-resolution increases the resolution of an image. Using evolutionary optimization, we optimize the noise injection of a super-resolution method for improving the results. More generally, our approach can be used to optimize any method based on noise injection.
Baptiste Rozière +6 more
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Super-resolution increases the resolution of an image. Using evolutionary optimization, we optimize the noise injection of a super-resolution method for improving the results. More generally, our approach can be used to optimize any method based on noise injection.
Baptiste Rozière +6 more
openaire +2 more sources
Exploiting Diffusion Prior for Real-World Image Super-Resolution
International Journal of Computer Vision, 2023We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without ...
Jianyi Wang +4 more
semanticscholar +1 more source
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
European Conference on Computer Vision, 2018Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train.
Yulun Zhang +5 more
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ECCV Workshops, 2018The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts.
Xintao Wang +8 more
semanticscholar +1 more source
Physics Letters A, 2003
In 1952 Toraldo di Francia [Il Nuovo Cimento (Suppl.) 9 (1952) 426] proposed an intriguing concept to increase the optical resolving power. As a consequence, this theory could seem in contradiction with Heisenberg's uncertainty principle. In this Letter we report on the results of a microwave investigation which demonstrate the rightness of such ...
Mugnai D, Ranfagni A, Ruggeri R
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In 1952 Toraldo di Francia [Il Nuovo Cimento (Suppl.) 9 (1952) 426] proposed an intriguing concept to increase the optical resolving power. As a consequence, this theory could seem in contradiction with Heisenberg's uncertainty principle. In this Letter we report on the results of a microwave investigation which demonstrate the rightness of such ...
Mugnai D, Ranfagni A, Ruggeri R
openaire +3 more sources
On Hyperspectral Super-Resolution
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021In this paper we will review seminal contributions of Prof. Jose Bioucas Dias for the improvement of the spatial resolution of hyperspectral images. Be it through the extension of pansharpening algorithms with spatial and spectral sparsity priors, using spectral unmixing, using a low-rank assumption from complementary multisource data, or by designing ...
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Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2005
A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an
Assaf Zomet, Alex Rav-Acha, Shmuel Peleg
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A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an
Assaf Zomet, Alex Rav-Acha, Shmuel Peleg
openaire +1 more source