A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
We come up with a conditional variational autoencoder to encode the reference for dense feature vector which can then be transferred to the decoder for target image denoising. ...
In this paper, we revisit the classic example based image super-resolution approaches and come up with a novel generative model for perceptual image super-resolution. ...
This is also the first work on combining Variational AutoEncoder and Generative Adversarial Network for image super-resolution. 3. ...
doi:10.1109/cvprw50498.2020.00229
dblp:conf/cvpr/LiuSWLCC20
fatcat:pefpfnflxrcw5jutze5iphey4u
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We come up with a conditional variational autoencoder to encode the reference for dense feature vector which can then be transferred to the decoder for target image denoising. ...
In this paper, we revisit the classic example based image super-resolution approaches and come up with a novel generative model for perceptual image super-resolution. ...
This is also the first work on combining Variational AutoEncoder and Generative Adversarial Network for image super-resolution. 3. ...
arXiv:2004.12811v1
fatcat:iwrvcyfg4jamjijp3qv2j2eoi4
Reference based Face Super-resolution
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We propose a novel Conditional Variational AutoEncoder model for this Reference based Face Super-Resolution (RefSR-VAE). ...
We create a benchmark dataset on reference based face super-resolution (RefSR-Face) for general research use, which contains reference images paired with low-resolution images of various pose, emotions ...
In this paper, we propose a novel Conditional Variational AutoEncoder model for Reference based Face Super-Resolution (RefSR-VAE). ...
doi:10.1109/access.2019.2934078
fatcat:qw6mh56ysvfpjbvno4hxn7sgwq
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To address this issue, we propose a learning-based super-resolution approach that can be trained using solely anisotropic images, i.e. without high-resolution ground truth data. ...
The method exploits the latent space, generated by autoencoders trained on anisotropic images, to increase spatial resolution in low-resolution images. ...
ACKNOWLEDGMENTS This study was performed within the DLMedIA program (P15-26) funded by Dutch Technology Foundation with participation of PIE Medical Imaging. ...
arXiv:2010.13172v1
fatcat:uqtgld3zdzht3ia4ql3rv6yvma
Image Super-Resolution With Deep Variational Autoencoders
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Models based on Variational Autoencoders (VAEs) have often been criticized for their feeble generative performance, but with new advancements such as VDVAE (very deep VAE), there is now strong evidence ...
Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. ...
Conclusions In this paper, we propose VDVAE-SR, a Very Deep Variational Autoencoder (VDVAE) adapted for the task of image super-resolution (SR). ...
arXiv:2203.09445v1
fatcat:ymed7hhhenfkbaxhi3vom3qyle
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Recently, better performing deep-learning based super-resolution methods have been introduced. ...
High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. ...
ACKNOWLEDGMENT This study was performed within the DLMedIA program (P15-26) funded by Dutch Technology Foundation with participation of Pie Medical Imaging. ...
arXiv:2202.09258v1
fatcat:ctyoztnoprg7vb3hgqwaljt6nq
Image Restoration using Autoencoding Priors
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We demonstrate state of the art results for non-blind deconvolution and super-resolution using the same autoencoding prior. ...
A key advantage of our approach is that we do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution with different kernels, or super-resolution at different ...
Classical techniques include priors based on edge statistics, total variation, sparse representations, or patch-based priors. ...
arXiv:1703.09964v1
fatcat:7f4tpcm6jvforcxufntm3c5lea
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI ( ...
Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner. ...
They would like to thank NCALM and the Hyperspectral Image Analysis Laboratory at UH for providing the UH datasets, and the Image Analysis and Data Fusion Technical Committee of the IEEE GRSS for supporting ...
arXiv:2110.11591v1
fatcat:6rxoim4qv5g3vejajliv3fqvpa
Bandwidth Extension on Raw Audio via Generative Adversarial Networks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this work we explore a GAN-based method for audio processing, and develop a convolutional neural network architecture to perform audio super-resolution. ...
Neural network-based methods have recently demonstrated state-of-the-art results on image synthesis and super-resolution tasks, in particular by using variants of generative adversarial networks (GANs) ...
Stacked autoencoders [43] and variational autoencoders [21, 37] have been used for denoising, image generation, and music synthesis [34] . ...
arXiv:1903.09027v1
fatcat:sbpiyc5kjjc3zj54l2funla6pu
Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an exemplar dataset. ...
We demonstrate that supplementing residual images or feature maps with additional facial attribute information can significantly reduce the ambiguity in face super-resolution. ...
Aimed at addressing pose variations, part based methods super-resolve individual facial regions separately. ...
doi:10.1109/cvpr.2018.00101
dblp:conf/cvpr/YuFHP18
fatcat:eevz23ankvc6varp32hqgk67pu
VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
VEHiCLE utilizes a variational autoencoder and adversarial training strategy to enhance contact maps, making them more viable for downstream analysis. ...
Using a variational autoencoder VEHiCLE provides a user tunable, full, generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across ...
The use of autoencoders for the task of Hi-C data super resolution was originally proposed in our preprint 10 for the task of denoising Hi-C data. ...
doi:10.1101/2020.12.07.413559
fatcat:j6wry3vugbbsdd5xlwbcmq6amm
VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data
2021
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Using a deep variational autoencoder, VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data ...
VEHiCLE expands previous efforts at Hi-C super resolution by providing novel insight into the biologically meaningful and human interpretable feature extraction. ...
The use of autoencoders for the task of Hi-C data super resolution was originally proposed in our preprint 10 for the task of denoising Hi-C data. ...
doi:10.1038/s41598-021-88115-9
pmid:33893353
fatcat:iabf3lhfhbe3xgqb2oxpsjmmna
Learning to generate images with perceptual similarity metrics
2017
2017 IEEE International Conference on Image Processing (ICIP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. ...
For three different architectures, we collected human judgments of the quality of image reconstructions. ...
Visual comparisons on super-resolution at a magnification factor of 4. ...
doi:10.1109/icip.2017.8297089
dblp:conf/icip/SnellRLRMZ17
fatcat:ykczprlhjbhzbax5btzs7vnjai
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
For example, sparsity or low-rankness based regularizers have been widely used for image reconstruction from limited data such as in compressed sensing. ...
Learning-based approaches for image reconstruction have garnered much attention in recent years and have shown promise across biomedical imaging applications. ...
Autoencoders have been incorporated into MBIR frameworks to provide learning-based priors, but most of these works train autoencoders in a supervised manner based on paired noisy and reference images ...
arXiv:2103.14528v1
fatcat:kxzugqnnijdwfn62jwrl45zmge
Learning to Generate Images with Perceptual Similarity Metrics
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. ...
For three different architectures, we collected human judgments of the quality of image reconstructions. ...
Image Super-Resolution Details For the super-resolution experiments, all input images are converted from RGB to YCbCr color space and only Y channel is used for training and testing. ...
arXiv:1511.06409v3
fatcat:ig2kqqvucjcihbogroo5djy3bi
« Previous
Showing results 1 — 15 out of 1,836 results