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Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
2022
Applied Sciences
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To deal with this issue, we propose a gradient-guided and multi-scale feature network for image super-resolution (GFSR). ...
Recently, deep-learning-based image super-resolution methods have made remarkable progress. ...
[34] proposed a holistic attention network (HAN) for image super-resolution. ...
doi:10.3390/app12062935
fatcat:7n23dbbzj5bjddo2nzhrm4axnq
Image super-resolution algorithm based on RRDB model
2021
IEEE Access
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This work was financially supported in part by Research on the influence of the evolution of new generation network and information technology on Zhejiang media and its development trend (20NDYD022YB). ...
INTRODUCTION Super-resolution reconstruction of a single image is a technique for recovering high-resolution images from lowresolution images. ...
The existing super-resolution network models based on the attention mechanism usually use channel attention and spatial attention networks. ...
doi:10.1109/access.2021.3118444
fatcat:u26grayknrh2ti4ngi3ofyz6si
MESR: Multistage Enhancement Network for Image Super-Resolution
2022
IEEE Access
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To this end, a multi-stage enhancement image network for super-resolution (MESR) is proposed. ...
The network consists of two stages, where the first stage is used to generate a coarse reconstructed image, and the second one is to refine the coarse image, which enhances the super-resolution performance ...
To achieve more noteworthy refinement of the reconstructed images, Lai et al. [35] proposed a deep laplacian pyramid network for super-resolution (LapSRN). ...
doi:10.1109/access.2022.3176605
fatcat:hlbeh6hq2janppw37pidw7dmfa
Super-resolution reconstruction method of face image based on attention mechanism
2021
IEEE Access
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In recent years, convolutional neural network in Single image super-resolution field show good results. ...
Deep networks can establish complex mapping between low-resolution and high-resolution images, making the reconstructed images quality a great progress over traditional methods. ...
INTRODUCTION Single Image Super-Resolution (SISR) is a low-level computer vision task. ...
doi:10.1109/access.2021.3070898
fatcat:lpopl3pp7nd2bbmkuse6gclghe
Hybrid Domain Attention Network for Efficient Super-Resolution
2022
Symmetry
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Specifically, the spatial self-attention module identifies important regions in the image, and the channel self-attention module adaptively emphasizes important channels. ...
This paper proposes a symmetric CNN (HDANet), which is based on the Transformer's self-attention mechanism and uses symmetric convolution to capture the dependencies of image features in two dimensions ...
[7] first applied CNN networks to single-image super-resolution reconstruction, proposing a three-layer convolutional neural network, SRCNN, and since then, more and more studies have tried to use CNN ...
doi:10.3390/sym14040697
fatcat:gxkypgoumnghpcjbjfuwqz6rce
Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image
2021
Sensors
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In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. ...
In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. ...
In this paper, our super-resolution reconstruction network for medical CT image references the residual network, attention mechanism and information distillation. ...
doi:10.3390/s21206870
pmid:34696083
pmcid:PMC8539557
fatcat:x64ockympndy3duyh3bzkzavcy
Adapting Image Super-Resolution State-Of-The-Arts and Learning Multi-Model Ensemble for Video Super-Resolution
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. ...
In this paper, we investigate how to adapt state-ofthe-art methods of image super-resolution for video superresolution. The proposed adapting method is straightforward. ...
Adapting Image Super-resolution State-of-thearts for Video Super-resolution We propose to learn deep spatial-temporal features for up-sampling video frames by adapting multiple state-ofthe-art image super-resolution ...
doi:10.1109/cvprw.2019.00255
dblp:conf/cvpr/LiHLDW19
fatcat:aoyu672zgvfv7eahsvu3a5kx2y
Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution
2020
IEEE Access
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INDEX TERMS Channel-wise sub-network attention, convolutional neural networks, multi-resolution subnetworks, single image super-resolution. ...
To overcome this problem, we propose a Multi-resolution space-Attended Residual Dense Network (MARDN) to separate lowfrequency and high-frequency information for reconstructing high-quality super-resolved ...
Her current study interests are in single image super-resolution and deep learning. ...
doi:10.1109/access.2020.2976478
fatcat:itvsommeqvf35jclkmhodecrou
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
[article]
2020
arXiv
pre-print
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Extensive experimentation and evaluations against other available solutions, either for single or multi-image super-resolution, have demonstrated that the proposed deep learning-based solution can be considered ...
state-of-the-art for Multi-Image Super-Resolution for remote sensing applications. ...
There are two main methods used in Super-resolution: Single-image SR (SISR) and Multi-image SR (MISR). SISR employs a single image to reconstruct a HR version of it. ...
arXiv:2007.03107v2
fatcat:cdyx7nxxnnaxhczwoxggdsjdkm
Advances in deep learning for real-time image and video reconstruction and processing
2020
Journal of Real-Time Image Processing
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Deep learning for image reconstruction and processing is a relatively new area. ...
Reconstructing image is a central problem in many key applications including super-resolution imaging, X-ray tomography, ultrasound imaging, remote sensing, and magnetic resonance imaging. ...
The paper "Optimised Highway Deep Learning Network for Fast Single Image Super-Resolution Reconstruction", aims at developing a novel model for single image super resolutions by using multi-scale connections ...
doi:10.1007/s11554-020-01026-2
fatcat:23jzdzkoxfdnrjfeew7bpwy7fm
Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution
2021
IEEE Access
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INDEX TERMS Remote sensing, super-resolution, feedback mechanism, ghost module, attention mechanism. ...
The super-resolution method can effectively restore the low-resolution image to the high-resolution image. ...
CONCLUSION In this paper, we propose the feedback ghost residual dense network (FGRDN) of single-image super-resolution. ...
doi:10.1109/access.2021.3052946
fatcat:zndked7xizfrrjdxwe7fihthnm
Orientation-Aware Deep Neural Network for Real Image Super-Resolution
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Orientation-aware features extracted in different directions are adaptively combined through a channel-wise attention mechanism to generate more distinctive features for high-fidelity recovery of image ...
In this paper, we proposed a novel orientation-aware deep neural network (OA-DNN) model, which incorporate a number of orientation feature extraction and channel attention modules (OAMs), to achieve good ...
Introduction Single image super-Resolution (SISR) aims to recover corresponding high-resolution (HR) image from a single low-resolution (LR) image. ...
doi:10.1109/cvprw.2019.00246
dblp:conf/cvpr/ChenHSYCCTY19
fatcat:4g22xeovlfezlortcl46gjk2nm
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
[article]
2019
arXiv
pre-print
Preserved Fulltext
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Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. ...
In this paper, we investigate how to adapt state-of-the-art methods of image super-resolution for video super-resolution. The proposed adapting method is straightforward. ...
Adapting Image Super-resolution State-of-thearts for Video Super-resolution We propose to learn deep spatial-temporal features for up-sampling video frames by adapting multiple state-ofthe-art image super-resolution ...
arXiv:1905.02462v1
fatcat:g3kannrxwbhojmbnouinrthht4
LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
2022
Sensors
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Spectral super-resolution (SSR) is a method that involves learning the relationship between a multispectral image (MSI) and an HSI, based on the overlap region, followed by reconstruction of the HSI by ...
However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a large coverage area cannot be acquired in a ...
[32] proposed a deep residual single-image super-resolution network which uses a channel attention mechanism. Zhang et al. ...
doi:10.3390/s22051978
pmid:35271131
pmcid:PMC8914896
fatcat:sqgidsrk4bhqvfmbmlswgicrja
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
[article]
2018
arXiv
pre-print
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The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). ...
To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation ...
Abstract-The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). ...
arXiv:1809.11130v1
fatcat:r6f2nb5knvayjayzz33tboapqe
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