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Learning Texture Transformer Network for Image Super-Resolution
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively. ...
We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. ...
To address these problems, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR). ...
arXiv:2006.04139v2
fatcat:jkaohnhydjegdfja7fpkhp5eoq
Super-Resolution reconstruction method of remote sensing image based on multi-feature fusion
2020
IEEE Access
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Among many methods, the super-resolution reconstruction method based on generative adversarial networks has obvious advantages over previous network models in reconstructing image texture details. ...
Aiming at the problem of whether the texture details of the reconstructed image are accurate and clear, we propose a super-resolution reconstruction method combining wavelet transform and generative adversarial ...
This method takes advantage of the advantages of wavelet transform and generative adversarial networks in the super-resolution field, so that the reconstructed image has better texture information and ...
doi:10.1109/access.2020.2967804
fatcat:hktuogb2rzes5gwtfkloi6iq7y
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
[article]
2020
arXiv
pre-print
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Then each sub-band generative adversarial network (sub-band GAN) conquers the super-resolution procedure of each single sub-band image. ...
In addition, inverse discrete wavelet transformation (IDWT) is integrated into model for taking the reconstruction of whole image into account. ...
For example, Cherukuri [8] enhanced deep MR image super-resolution by a deep network that exploits a low-rank structure and a sharpness prior of MR images. ...
arXiv:2011.04145v1
fatcat:cn4vax73o5b3pdk6n2cne23ney
Super-resolution of geosynchronous synthetic aperture radar images using dialectical GANs
2019
Science China Information Sciences
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Acknowledgements We thank the Alaska Satellite Facility for the ALOS PALSAR images. ...
Although deep learning has produced remarkable super-resolution results for optical images, little attention has been paid to super-resolution for SAR images [7] . ...
Di-GANs generate super-resolution images (SRIs) by intelligently learning the texture of input style images while simultaneously preserving the information conveyed by the input content images rather than ...
doi:10.1007/s11432-018-9668-6
fatcat:uol4v2zr2vbrnfq4vk7wgfg6oy
Crop Leaf Disease Image Super-Resolution and Identification with Dual Attention and Topology Fusion Generative Adversarial Network
2020
IEEE Access
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This network can effectively transform unclear images into clear and high-resolution images. ...
INDEX TERMS Crop leaf disease, attention, generative adversarial networks, super-resolution, identification. 55724 This work is licensed under a Creative Commons Attribution 4.0 License. ...
for image super-resolution tasks. ...
doi:10.1109/access.2020.2982055
fatcat:byx6nmhyb5hx3jj6vqwj4kvjtm
Learning wavelet coefficients for face super-resolution
2020
The Visual Computer
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To overcome this problem, we propose a novel deep neural network to predict the super-resolution wavelet coefficients in order to obtain clearer facial images. ...
Finally, image edge features from canny detector are applied to enhance super-resolution images during training. ...
Introduction Face Super-Resolution (SR) is an important subset of image super-resolution technology for public security. ...
doi:10.1007/s00371-020-01925-2
fatcat:lqnjva6ttrgk7cxinwcbo5gupi
Learned Multi-View Texture Super-Resolution
[article]
2020
arXiv
pre-print
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We map that inverse problem into a block of suitably designed neural network layers, and combine it with a standard encoder-decoder network for learned single-image super-resolution. ...
We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
arXiv:2001.04775v1
fatcat:eo2xo6frrvcwfkute62h73yx5u
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution
[article]
2021
arXiv
pre-print
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Our goal is to generate high-resolution synthetic X-ray images in real-time by upsampling low-resolution images with deep learning-based super-resolution methods. ...
To the best of our knowledge, this is the first paper utilizing the frequency domain for the loss functions in the field of super-resolution. ...
Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning Texture Transformer Network for
Image Super-Resolution. arXiv:2006.04139 [cs]. (2020).
14. ...
arXiv:2105.06887v2
fatcat:54lwabxhynhpbhhxpivoxk2npq
Recent trends in image processing and pattern recognition
2020
Multimedia tools and applications
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In "Super Resolution of Single Depth Image based on Multi-dictionary Learning with Edge Feature Regularization," authors focused on super resolution based on multi-dictionary learning with edge regularization ...
In "Super-Resolution Quality Criterion (SRQC): A Super-Resolution Image Quality Assessment Metric," authors reported the importance of SRQC in assessing image quality. ...
doi:10.1007/s11042-020-10093-3
fatcat:z2wzbhk4qbd3vpsyuzm4pdlque
Perceptual Extreme Super Resolution Network with Receptive Field Block
[article]
2020
arXiv
pre-print
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Perceptual Extreme Super-Resolution for single image is extremely difficult, because the texture details of different images vary greatly. ...
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. ...
For image super-resolution, VDSR [17] reveals that increasing network depth shows a significant improvement in SISR. ...
arXiv:2005.12597v1
fatcat:4tjretl65nc2hm5uzx5tua652u
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
[article]
2018
arXiv
pre-print
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Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging ...
This is made possible through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. ...
[42] propose context-constrained super-resolution by learning from texturally similar training segments. Timofte et al. ...
arXiv:1804.02815v1
fatcat:xbj24rdj2rcprmg63vf5fqlh5y
A Review of Deep Learning Based Image Super-resolution Techniques
[article]
2022
arXiv
pre-print
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in the field of image super-resolution, and reports the latest progress of image super-resolution technology based on depth learning method. ...
With the development of deep learning, image super-resolution technology based on deep learning method is emerging. ...
[7] First applied the deep learning method to the field of image super-resolution, proposed a three-layer convolution neural network model for image super-resolution reconstruction, described it as ...
arXiv:2201.10521v1
fatcat:ul5sxm3ssfagbc6nzpfvgzhowi
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
[article]
2021
arXiv
pre-print
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Recently, deep learning and generative adversarial networks(GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). ...
Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from aremotely sensed image with low spatial resolution. ...
EXPERIMENT
Train the network via Transfer learning The super-resolution of deep-learning spatio-temporal remote sensing images has more problems than the natural one. ...
arXiv:2104.10268v1
fatcat:oznne3a7xjhbtej6wgkdf3og7i
Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
[article]
2021
arXiv
pre-print
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First, random noise maps are fed into a designed generative adversarial network (GAN) for reconstruction. ...
To improve image resolution, numerous approaches based on training low-high resolution pairs have been proposed to address the super-resolution (SR) task. ...
Super-resolution (SR) aims to reconstruct the high spatial resolution (HSR) image from observed low spatial resolution (LSR) images [1] , which breaks the limitations of the imaging system for the best ...
arXiv:2105.03579v2
fatcat:lxbdymxcwvcbbigisumkgm246y
Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution
2017
Cognitive Computation
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for image super-resolution tend to exploit an indiscriminate scheme for processing one whole image. ...
We then train K convolutional neural networks for super-resolution based on the K clusters of patches separately, such that the multiple convolutional neural networks comprehensively capture the patch ...
Ethical Standards Funding: This study was funded by National Natural Science Foundation of China (No. 61671481) and Qingdao Applied Fundamental Research (No. 16-5-1-11-jch), the Fundamental Research Funds for ...
doi:10.1007/s12559-017-9512-2
fatcat:2rw33efvjrh53lw4dejxochyy4
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