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Image Super-resolution with An Enhanced Group Convolutional Neural Network
[article]
2022
arXiv
pre-print
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An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. ...
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information ...
Conclusion This paper presents an enhanced super-resolution group CNN (ESRGCNN) for SISR. ...
arXiv:2205.14548v2
fatcat:bh77pgmehfdkpg4tu4unyoyx3a
Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network
2017
Open Journal of Medical Imaging
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Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. ...
We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. ...
method; and (f) super-resolution convolutional neural network method. ...
doi:10.4236/ojmi.2017.74018
fatcat:mwslqylv6fbftn63346hh3gppq
Lightweight Single Image Super-Resolution with Similar Feature Fusion Block
2022
IEEE Access
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Convolutional neural network-based image super-resolution methods have achieved great success in recent years. ...
INDEX TERMS Convolutional neural networks, lightweight, image super-resolution, similar feature fusion. ...
[1] first proposed a three-layer super-resolution convolutional neural network (SRCNN). Since then, several superresolution methods based on convolutional neural networks have been explored. ...
doi:10.1109/access.2022.3158936
fatcat:bhs7ujplvff4nbxly6cpnrty7a
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution
[article]
2024
arXiv
pre-print
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We propose a lightweight convolutional neural network (CNN)-based algorithm which simultaneously performs artifacts reduction and super resolution (ARSR) by enhancing the feature extraction layers and ...
Super resolution-only approaches will amplify the artifacts along with the details by default. ...
CNN-based solution to super resolution, with an artifacts reduction approach used in AR-CNN [6] . ...
arXiv:2401.14641v1
fatcat:efjneaixafbwdknrfdztarpgc4
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework
[article]
2023
arXiv
pre-print
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NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details ...
denoising, super resolution, interpolation, and spatio-temporal super-resolution. ...
Compared to image super resolution, video super resolution can utilize temporal information between frames to get better result, but also faces an extra challenge to aggregating information among misaligned ...
arXiv:2308.03121v1
fatcat:jstr2ngluzamfgbqob4xj2u2iu
Image Super-resolution Using Mid-level Representations
2016
DEStech Transactions on Engineering and Technology Research
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An end-to-end six layers convolutional neural network(CNNs) structure is proposed to realize single image super-resolution reconstruction. ...
The input of the network is low-resolution (LR) image, and the output is the superresolution (SR) image. Promising experimental results are obtained with higher precision. ...
Inspiring by the paper of Maxime Oquab [7] , we try to learn and transfer Mid-level image representations and construct an end-to-end six layers convolutional neural network for image super-resolution ...
doi:10.12783/dtetr/iect2016/3750
fatcat:7aqy4lax5bgj7i5v5b3frv6zsi
Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution
2017
Cognitive Computation
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Conclusion: Our multiple convolutional neural network framework provides an enhanced image superresolution strategy over existing single-mode deep learning models. ...
super-resolution tend to exploit an indiscriminate scheme for processing one whole image. ...
Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. ...
doi:10.1007/s12559-017-9512-2
fatcat:2rw33efvjrh53lw4dejxochyy4
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images
[article]
2022
arXiv
pre-print
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Hence, we performed a comprehensive study to prove our point that, enhancement of resolution by Super-Resolution Convolutional Neural Network (SRCNN) will lessen the chance of misclassification of pixels ...
However, freely accessible satellite images are, generally, of medium to low resolution which is a major hindrance to the precision of the analysis. ...
Super Resolution Convolutional Neural Network SRCNN produces expanded images with improved details. ...
arXiv:2207.02301v1
fatcat:5iqspduklfgwzmkyyehbbynrka
Super-Resolution using Deep Learning to Support Person Identification in Surveillance Video
2020
International Journal of Advanced Computer Science and Applications
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More specifically, we used the Very-Deep Super-Resolution (VDSR) neural network to enhance the image quality. ...
The proposed system relies on an image processing technique called Super-Resolution that consists of recovering high-resolution images from low-resolution ones. ...
SISR for Generic Images using Deep Learning The authors in [6] , proposed an SR system called Super-Resolution Convolutional Neural Network (SRCNN). ...
doi:10.14569/ijacsa.2020.0110749
fatcat:2tq5y3hujbharowhtj2h4cjsp4
Real-Time Video Scaling Based On Convolution Neural Network Architecture
2017
Indonesian Journal of Electrical Engineering and Computer Science
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In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction ...
Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. ...
In [27], Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network applied. ...
doi:10.11591/ijeecs.v7.i2.pp381-394
fatcat:fvqcahgpqjh6dd2j27rrokckti
Learning Based Super Resolution Application for Hyperspectral Images
2021
International scientific and vocational studies journal
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First the application obtains a super-resolution image from a single hyperspectral image with a low spatial image with a deep convolutional neural network. ...
Later, the super-resolution image obtained, and the original low-spatial-resolution hyperspectral image are fused with the dictionary learning method, resulting in a new super-resolution image with high ...
The convolutional neural network model learns an end-to-end spectral difference mapping between low spatial resolution hyperspectral image and super-resolution hyperspectral image. ...
doi:10.47897/bilmes.1049338
fatcat:wayekvbxkngn7mn4yhwxvocpqi
Deep learning for multisensor image resolution enhancement
2017
Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery - GeoAI '17
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We describe a deep learning convolutional neural network (CNN) for enhancing low resolution multispectral satellite imagery without the use of a panchromatic image. ...
The trained network can then be effectively used for super-resolution enhancement of low resolution multispectral images where no corresponding high resolution image is available. ...
The network we use for the enhancement of satellite imagery is based on the ground-breaking super-resolution convolutional neural network (SRCNN) proposed by Dong et al. [11] . ...
doi:10.1145/3149808.3149815
dblp:conf/gis/CollinsBBRG17
fatcat:l6zftbbenvhgzmxtafbh3jx2fy
A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications
2021
Complex & Intelligent Systems
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In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. ...
Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. ...
Super-resolution CNNs (SRCNN) are accompanied by the image super-resolution Deeply-Recursive Convolutional Neural (DRCN) [29] . ...
doi:10.1007/s40747-021-00465-z
fatcat:c2t3qd4yjvhhfgggi5d6pulfx4
Pre‐training of gated convolution neural network for remote sensing image super‐resolution
2021
IET Image Processing
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Many very deep neural networks are proposed to obtain accurate super-resolution reconstruction of remote sensing images. ...
To solve these problems, a novel single-image superresolution algorithm named pre-training of gated convolution neural network (PGCNN) is proposed for remote sensing images. ...
And compared with the traditional algorithm, deep convolution neural networks show an overwhelming advantage to deal with super-resolution (SR) problems [5, 6] . ...
doi:10.1049/ipr2.12096
fatcat:sr5bro5sw5gctp5twvw2lbtriq
Lightweight Single Image Super-Resolution by Channel Split Residual Convolution
2022
Journal of Information Processing Systems
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In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. ...
Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution. ...
image super-resolution. ...
doi:10.3745/jips.02.0168
dblp:journals/jips/Liu22
fatcat:ehpigbakp5hxblsqf4cgsjy4yi
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