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Robust Super-Resolution GAN, with Manifold-based and Perception Loss
[article]
2019
arXiv
pre-print
Preserved Fulltext
We propose a loss based on an autoencoder-based manifold-distance between the super-resolved and high-resolution images, to reproduce realistic textural content in super-resolved images. ...
We propose to learn to super-resolve images to match human perceptions of structure, luminance, and contrast. ...
We propose a novel GAN-based learning framework for super-resolution that is robust to errors in training-set curation, using quasi-norm based loss functions. ...
arXiv:1903.06920v1
fatcat:q53u5tgy5jbhtftadtsdianwdi
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
[article]
2019
arXiv
pre-print
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Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones. ...
However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. ...
Natural and Realistic Super-Resolution In this section, we explain the proposed natural and realistic super-resolution (NatSR) generator model and the training loss function. ...
arXiv:1911.03624v1
fatcat:4axaj7zrhrdxvmikvgdpobdyam
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones. ...
However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. ...
Natural and Realistic Super-Resolution In this section, we explain the proposed natural and realistic super-resolution (NatSR) generator model and the training loss function. ...
doi:10.1109/cvpr.2019.00831
dblp:conf/cvpr/SohPJC19
fatcat:nnh34jnkmfdjhb7tkmgoahauji
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
[article]
2021
arXiv
pre-print
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AVIRIS and UHD-185) for various upscaling factors and added noise levels, and compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR, BAGAN, SR- GAN, WGAN). ...
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. ...
Strategy (BEIS)) and the Open Research Fund of Key Laboratory of Digital Earth Science, Chinese Academy of Sciences(No.2019LDE003). ...
arXiv:2111.08685v1
fatcat:xfabty4425f2ljiwdjpwyonxd4
Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks
[chapter]
2019
Lecture Notes in Computer Science
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This paper considers a deep Generative Adversarial Networks (GAN) based method referred to as the Perception-Enhanced Super-Resolution (PESR) for Single Image Super Resolution (SISR) that enhances the ...
perceptual quality of the reconstructed images by considering the following three issues: (1) ease GAN training by replacing an absolute with a relativistic discriminator, (2) include in the loss function ...
Conclusion We have presented a deep Generative Adversarial Network (GAN) based method referred to as the Perception-Enhanced Super-Resolution (PESR) for Single Image Super Resolution (SISR) that enhances ...
doi:10.1007/978-3-030-11021-5_7
fatcat:v6mizuzxzrctnoize3xcpf7d4i
Manifold Matching via Deep Metric Learning for Generative Modeling
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2106.10777v2 fatcat:47mlcn3uj5fb3ec4lhraase3hqOther Versions
; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. ...
It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and p-diameter, with learned distance metric; the metric generator utilizes both real data and generated ...
Final results with both distortion-based and perception-based evaluation scores on benchmark datasets are presented in Table 5 , where GAN-ResNet represents SRGAN without VGG component in loss function ...
arXiv:2106.10777v3
fatcat:eabulpgy6bcexjfmcivhin2pia
Best-Buddy GANs for Highly Detailed Image Super-Resolution
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.15295v2 fatcat:yg355fm3zrgunow7nr6exdhcvuOther Versions
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. ...
An ultra-high-resolution 4K dataset is also constructed to facilitate future super-resolution research. ...
Ground-truth-based LPIPS is more representative and robust than no-reference-based PI. Zoom in for better visual comparison. ...
arXiv:2103.15295v3
fatcat:xljujqfacjdevnmgn5vdrdq764
Creating High Resolution Images with a Latent Adversarial Generator
[article]
2020
arXiv
pre-print
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Single image super-resolution is the task of predicting the image closest to the ground truth from a relatively low resolution image. ...
We propose to produce samples of high resolution images given extremely small inputs with a new method called Latent Adversarial Generator (LAG). ...
In the context of super-resolution from a low resolution (LR) input, this task has been researched extensively and the current best methods are based on deep learning (DL). ...
arXiv:2003.02365v1
fatcat:w33o32dwobbzlo3jltvqrok25u
Perceptual Extreme Super Resolution Network with Receptive Field Block
[article]
2020
arXiv
pre-print
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To tackle this difficulty, we develop a super resolution network with receptive field block based on Enhanced SRGAN. We call our network RFB-ESRGAN. The key contributions are listed as follows. ...
First, for the purpose of extracting multi-scale information and enhance the feature discriminability, we applied receptive field block (RFB) to super resolution. ...
We also attempt to fuse the models with more GAN-based models. For instance, use 20 or 40 best GAN-based models for ensemble. ...
arXiv:2005.12597v1
fatcat:4tjretl65nc2hm5uzx5tua652u
One-to-many Approach for Improving Super-Resolution
[article]
2021
arXiv
pre-print
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In this paper, we adapt the concepts of SRFlow to improve GAN-based super-resolution by properly implementing the one-to-many property. ...
Recently, there has been discussions on the ill-posed nature of super-resolution that multiple possible reconstructions exist for a given low-resolution image. ...
We believe the current GAN-based solutions for super-resolution is missing key components of a one-to-many pipeline. ...
arXiv:2106.10437v4
fatcat:ezbeaptaqjbtjh32wdrkqlxgze
Advanced Single Image Resolution Upsurging Using A Generative Adversarial Network
2020
Signal & Image Processing An International Journal
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2020 pre-print arXiv:2006.00186v1 fatcat:ttgiu3bogbf63gbmwfqrk6476y
A higher resolution of an image is important for various fields such as medical imaging; astronomy works and so on as images of lower resolution becomes unclear and indistinct when their sizes are enlarged ...
In this paper, we have proposed a technique of generating higher resolution images form lower resolution using Residual in Residual Dense Block network architecture with a deep network. ...
Here we use loss function in (3) . Pre-training with loss based on pixel helps GAN-based methods to gain visually better results. ...
doi:10.5121/sipij.2020.11105
fatcat:cn2cifwd5bcsfezjeopmet47ru
Reference based Face Super-resolution
2019
IEEE Access
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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 ...
We propose a novel Conditional Variational AutoEncoder model for this Reference based Face Super-Resolution (RefSR-VAE). ...
Note that FSRGAN and FSRNet are the results from [35] . FSRGAN is the GAN version of FSRNet that was trained based on perception loss rather than pixel based MSE loss. ...
doi:10.1109/access.2019.2934078
fatcat:qw6mh56ysvfpjbvno4hxn7sgwq
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
[article]
2017
arXiv
pre-print
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In the framework, we propose a robust perceptual loss based on the discriminator of the built SRPGAN model. ...
We use the Charbonnier loss function to build the content loss and combine it with the proposed perceptual loss and the adversarial loss. ...
We can get a more robust perceptual loss for image super resolution by that. ...
arXiv:1712.05927v2
fatcat:b6bfrhmp3zflfkjprv75xuyrme
HRINet: Alternative Supervision Network for High-resolution CT image Interpolation
[article]
2020
arXiv
pre-print
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We compare an MSE based and a perceptual based loss optimizing methods for high quality interpolation, and show the tradeoff between the structural correctness and sharpness. ...
We combine the idea of ACAI and GANs, and propose a novel idea of alternative supervision method by applying supervised and unsupervised training alternatively to raise the accuracy of human organ structures ...
[2] , image super-resolution [3] [4] and neural style transfer [5] [6], etc. ...
arXiv:2002.04455v2
fatcat:6hgt7uab7vhcrkglzrxaevjwdy
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
., +, TIP 2021 9270-9279 VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR Multi-Stream Fusion Network With Generalized Smooth L 1 Loss for Single and CT Volumetric Data. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
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