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Deep Burst Super-Resolution
[article]
2021
arXiv
pre-print
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We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. ...
This key advantage, along with the increasing popularity of burst photography, have made MFSR an important problem for real-world applications. ...
Burst Super-Resolution Network In this section, we describe our burst super-resolution network. Our network inputs multiple noisy, RAW, lowresolution (LR) images captured in a single burst. ...
arXiv:2101.10997v2
fatcat:luxjrwxhpbcvpmxllapo5vinmq
Deep feature fusion for self-supervised monocular depth prediction
[article]
2020
arXiv
pre-print
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Our fusion network selects features from both upper and lower levels at every level in the encoder network, thereby creating multiple feature pyramid sub-networks that are fed to the decoder after applying ...
We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth using a pixel shuffling framework that super-resolves ...
We combine encoded features with the CoordConv solution thereby learning robust invariant features refined by a residual decoder that incorporates depth super resolution for learning fine-grained depth ...
arXiv:2005.07922v1
fatcat:lj7x2lteezanhfvcilz7wfxhwe
Joint Spatial and Angular Super-Resolution from a Single Image
[article]
2019
arXiv
pre-print
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The conventional method relies on physical-based rendering and a secondary network to solve the angular super-resolution problem. ...
In this paper, we show that both super-resolution problems can be solved jointly from a single image by proposing a single end-to-end deep neural network that does not require a physical-based approach ...
The idea is to jointly synthesize light field through angular and spatial super-resolution (SR) from only a single image which is abundantly available in the real world. ...
arXiv:1911.11619v2
fatcat:c6dvyo7bufg2xg6ip4phjx5e2y
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
[article]
2021
arXiv
pre-print
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Therefore, we proposed a novel super-resolution method that uses a generative adversarial network (GAN) with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution ...
Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. ...
The RED \cite{mao2016image} used an encoder-decoder framework in the super-resolution method. ...
arXiv:2107.09989v1
fatcat:4dmywsdypvbyhcefgu2lfqbx5m
Perceptually-inspired super-resolution of compressed videos
[article]
2021
arXiv
pre-print
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2019 Applications of Digital Image Processing XLII unpublished doi:10.1117/12.2530688 fatcat:zrzlyxkmtfcm7cxgl6bvelwh2u
This approach encodes a lower resolution version of the input video and reconstructs the original resolution during decoding. ...
In this paper, a perceptually-inspired super-resolution approach (M-SRGAN) is proposed for spatial up-sampling of compressed video using a modified CNN model, which has been trained using a generative ...
Input Video
Spatial Down-
sampling
Bitstream
Host Encoder
CNN-based
Super Resolution
Host Decoder
Final Reconstructed
Video (Full Resolution)
Decoded Low
Resolution Video
Low Resolution ...
arXiv:2106.08147v1
fatcat:etpxv745dfc3rngpehzkz7iwhi
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
2021
IEEE Access
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INDEX TERMS Super-resolution reconstruction, pelvic, generative adversarial network, cyclic loss, attention. ...
Therefore, we proposed a novel super-resolution method that uses a generative adversarial network with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images ...
The RED [11] used an encoder-decoder framework in the super-resolution method. ...
doi:10.1109/access.2021.3099695
fatcat:izcyarzbendp5ni64u2odr5tue
Joint Light Field Spatial and Angular Super-Resolution from a Single Image
2020
IEEE Access
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The conventional method relies on physical-based rendering and a secondary network to solve the angular super-resolution problem. ...
In this paper, we show that both super-resolution problems can be solved jointly from a single image by proposing a single end-to-end deep neural network that does not require a physical-based approach ...
The idea is to jointly synthesize light field through angular and spatial super-resolution (SR) from only a single image which is abundantly available in the real world. ...
doi:10.1109/access.2020.3002921
fatcat:xcxl6lp2hzfsljqeiufzmje3wi
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition
[article]
2021
arXiv
pre-print
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We achieved this by training a very deep convolutional encoder-decoder network with a symmetric contracting path between corresponding layers. ...
Here, we propose an identity-preserving end-to-end image-to-image translation deep neural network which is capable of super-resolving very low-resolution faces to their high-resolution counterparts while ...
[12] was to develop a residual neural network for superresolution that enhances gradient images. ...
arXiv:2010.12249v3
fatcat:dwjszfc6cvh5vmkuji35kerbxm
Light Field Image Compression via CNN-Based EPI Super-Resolution and Decoder-Side Quality Enhancement
2019
IEEE Access
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We propose a multi-scale dense residual network (MSDRN) to implement both EPI super-resolution and quality enhancement. ...
The epipolar plane image (EPI) super-resolution is for compensating the information loss caused by sparse sampling and the decoder-side sub-aperture images (SAIs) quality enhancement is for compensating ...
In order to implement both EPI super-resolution and decoder-side quality enhancement, we design a multi-scale dense residual network (MSDRN), which extracts features with multi-scale filters and passes ...
doi:10.1109/access.2019.2930644
fatcat:xjj63zrdrncltbhtmbvwp6hd2a
Video compression with low complexity CNN-based spatial resolution adaptation
[article]
2020
arXiv
pre-print
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This approach employs a CNN model for video down-sampling at the encoder and uses a Lanczos3 filter to reconstruct full resolution at the decoder. ...
However, this approach suffers from high complexity at the decoder due to the employment of CNN-based super-resolution. ...
Since the proposed network is used for resolution downsampling before encoding, the CNN output (at low resolution) should preserve sufficient spatial information to enble high fidelity full resolution ...
arXiv:2007.14726v1
fatcat:vzjsjjcd6zhotgcqvxa4l7f74u
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to superresolve very low-resolution ...
To generate realistic faces, we also propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. ...
Face Super-Resolution Network
Overview of FSRNet Our basic FSRNet F consists of four parts: coarse SR network, fine SR encoder, prior estimation network and finally a fine SR decoder. ...
doi:10.1109/cvpr.2018.00264
dblp:conf/cvpr/ChenT0S018
fatcat:5dcj4k2bsbhnzoepefzgfqpnte
Perceptual Image Super-Resolution with Progressive Adversarial Network
[article]
2020
arXiv
pre-print
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To address this issue, we propose Progressive Adversarial Network (PAN) that is capable of coping with this difficulty for domain-specific image super-resolution. ...
The low-level features in encoder can be transferred into decoder to enhance textural details with U-Net. ...
These connections are useful to save insights from different abstraction levels and transfer them from the encoder to the decoder network. ...
arXiv:2003.03756v4
fatcat:dg32vyec5ndhrhmpin7kp4uhwi
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images
[article]
2022
arXiv
pre-print
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Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images. ...
Specifically, we learn this intrinsic visual structure by encoding and decoding the degradation transformation from a pair of original and randomly degraded images. ...
By removing this layer, Lim et al. proposed enhanced deep super resolution network (EDSR) [38] that achieves the SOTA in 2017. ...
arXiv:2201.02314v1
fatcat:dhzdddkseffotjzwpehrhryocq
Lossless Image Compression through Super-Resolution
[article]
2020
arXiv
pre-print
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For lossless super-resolution, we predict the probability of a high-resolution image, conditioned on the low-resolution input, and use entropy coding to compress this super-resolution operator. ...
We store a low resolution version of an image as raw pixels, followed by several iterations of lossless super-resolution. ...
In the following section, we describe our network for one level of super-resolution and omit the superscript for simplicity. ...
arXiv:2004.02872v1
fatcat:ocwhfs5f55ht3o2dc3w3lfkcbe
Brain MRI Super-resolution Using 3D Dilated Convolutional Encoder-Decoder Network
2020
IEEE Access
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INDEX TERMS Magnetic resonance image, super-resolution reconstruction, dilated convolutional neural networks, encoder-decoder network. ...
To address this issue, we propose a novel dilated convolutional encoder-decoder (DCED) network to improve the resolution of MRI. ...
CONCLUSION In this paper, we propose to reconstruct high-resolution MRI through a dilated encoder-decoder network. ...
doi:10.1109/access.2020.2968395
fatcat:utlyrnabobazbnanxgqxutgtg4
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