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Medical image super-resolution method based on dense blended attention network [article]

Kewen Liu, Yuan Ma, Hongxia Xiong, Zejun Yan, Zhijun Zhou, Panpan Fang, Chaoyang Liu
2019 arXiv   pre-print
image super-resolution method based on dense neural network and blended attention mechanism is proposed.  ...  This work provides a new idea for theoretical studies of medical image super-resolution reconstruction.  ...  image super-resolution method based on dense neural network and blended attention mechanism is proposed.  ... 
arXiv:1905.05084v1 fatcat:m3ffckx26vb45ctmwhsfrxcldq

The Image Super-Resolution Algorithm Based on the Dense Space Attention Network (July 2020)

Chunjiang Duanmu, Junjie Zhu
2020 IEEE Access  
In the fifth row denoted by r5, it shows that when the dense network, CBAM, and global feature fusion are all employed, the average PSNR for the super-resolution images in the Set5 is 34.39dB.  ...  In Table I , the first row denoted by r1 shows that when the dense network, global feature fusion, and CBAM are all not used, the average PSNR value for the super-resolution images in the Set5 is only  ... 
doi:10.1109/access.2020.3013401 fatcat:2bbku6xfxnfufdyysaqrcw4dsm

Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

Jin Wang, Yiming Wu, Liu Wang, Lei Wang, Osama Alfarraj, Amr Tolba
2021 IEEE Access  
The super-resolution method can effectively restore the low-resolution image to the high-resolution image.  ...  INDEX TERMS Remote sensing, super-resolution, feedback mechanism, ghost module, attention mechanism.  ...  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

A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution

Lei Zha, Yu Yang, Zicheng Lai, Ziwei Zhang, Juan Wen
2021 Electronics  
In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training  ...  To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful  ...  [18] proposed the first Super-Resolution Convolutional Neural Network (SRCNN), introducing a three-layer convolution neural network for single image super-resolution.  ... 
doi:10.3390/electronics10111234 fatcat:drqxucnfwvapjgayioo4niswii

PAG-Net: Progressive Attention Guided Depth Super-resolution Network [article]

Arpit Bansal, Sankaraganesh Jonna, Rajiv R.Sahay
2019 arXiv   pre-print
It is based on residual dense networks and involves the attention mechanism to suppress the texture copying problem arises due to improper guidance by RGB images.  ...  In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet.  ...  Fig. 1 . 1 Architecture of the proposed progressive attention guided depth super-resolution (PAG-Net) network for 8x upsampling. Fig. 2 . 2 (a) RDN: Residual Dense Network.  ... 
arXiv:1911.09878v1 fatcat:ijjks33j7jdbhp73b3mp6sueym

Triple Attention Mixed Link Network for Single Image Super Resolution [article]

Xi Cheng, Xiang Li, Jian Yang
2018 arXiv   pre-print
Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive performance.  ...  of attention mechanisms and 2) fu-sion of both powerful residual and dense connections (i.e., mixed link).  ...  U1713208 and 61472187, the 973 Program No. 2014CB349303, and Program for Changjiang Scholars.  ... 
arXiv:1810.03254v1 fatcat:ebttd7xqpfgqjdgeamzmul7nny

A Review of Deep Learning Based Image Super-resolution Techniques [article]

Fangyuan Zhu
2022 arXiv   pre-print
Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images.  ...  in the field of image super-resolution, and reports the latest progress of image super-resolution technology based on depth learning method.  ...  Attention structure super-resolution usually designs the corresponding attention module for each part of the image.  ... 
arXiv:2201.10521v1 fatcat:ul5sxm3ssfagbc6nzpfvgzhowi

Crop Leaf Disease Image Super-Resolution and Identification with Dual Attention and Topology Fusion Generative Adversarial Network

Qiang Dai, Xi Cheng, Yan Qiao, Youhua Zhang
2020 IEEE Access  
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.  ...  This network can effectively transform unclear images into clear and high-resolution images.  ...  for image super-resolution tasks.  ... 
doi:10.1109/access.2020.2982055 fatcat:byx6nmhyb5hx3jj6vqwj4kvjtm

Agricultural Pest Super-Resolution and Identification with Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network

Qiang Dai, Xi Cheng, Yan Qiao, Youhua Zhang
2020 IEEE Access  
In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images.  ...  Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets  ...  CONCLUSION In this work, we propose a novel image super-resolution method for agricultural pest images.  ... 
doi:10.1109/access.2020.2991552 fatcat:26v3fenur5appffulqyixehvju

MAANet: Multi-view Aware Attention Networks for Image Super-Resolution [article]

Jingcai Guo, Shiheng Ma, Song Guo
2019 arXiv   pre-print
In most recent years, deep convolutional neural networks (DCNNs) based image super-resolution (SR) has gained increasing attention in multimedia and computer vision communities, focusing on restoring the  ...  These problems hinder the effectiveness of DCNNs in image SR task. To solve these problems, we propose the Multi-view Aware Attention Networks (MAANet) for image SR task.  ...  Image super-resolution using deep convolutional networks.  ... 
arXiv:1904.06252v1 fatcat:2q2b2xr7czgprmddh5u5salkte

SRPRID: Pedestrian Re-identification based on Super-resolution Images

Zhen Qin, Wei He, Fuhu Deng, Meng Li, Meng Li, Yao Liu
2019 IEEE Access  
INDEX TERMS Person re-identification, super resolution, residual dense block, soft attention, hard attention, convolutional neural networks, video surveillance.  ...  For more information, see  ...  Inspired by this, Gao et al. proposes SRDenseNet [23] that is used dense blocks for super resolution restruction.  ... 
doi:10.1109/access.2019.2948260 fatcat:7apjdecsdbej3c63rncnvmomq4

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results [article]

Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han (+64 others)
2020 arXiv   pre-print
The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications.  ...  This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.  ...  The kailos team proposed RRBD Network with Attention mechanism using Wavelet loss for Single Image Super-Resolution.  ... 
arXiv:2009.12072v1 fatcat:7cwsjfhqa5cf7avfvdabpmxrda

Triple-Attention Mixed-Link Network for Single-Image Super-Resolution

Xi Cheng, Xiang Li, Jian Yang
2019 Applied Sciences  
Single-image super-resolution is of great importance as a low-level computer-vision task. Recent approaches with deep convolutional neural networks have achieved impressive performance.  ...  ) of attention mechanisms and (2) fusion of both powerful residual and dense connections (i.e., mixed link).  ...  Abbreviations The following abbreviations are used in this manuscript: TAN Triple-attention mixed-link network CA Channel attention KA Kernel attention SA Spatial attention AE-MLB Attention-enhanced  ... 
doi:10.3390/app9152992 fatcat:azmvugentbaczcmnmgwgyifzga

Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network

Yunchuan Ma, Pengyuan Lv, Hao Liu, Xuehong Sun, Yanfei Zhong
2021 Remote Sensing  
In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing.  ...  To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images.  ...  In 2015, a super resolution convolutional neural network (SRCNN) [32] was first proposed by Dong et al. to achieve the natural images super resolution.  ... 
doi:10.3390/rs13152966 fatcat:r2m275vddfctza63cxacshyukm

Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

Jiang, Huang, Hu
2020 Applied Sciences  
A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the  ...  The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN).  ...  The super-resolution convolutional neural network (SRCNN) [11] algorithm introduced deep learning methods to SISR for the first time.  ... 
doi:10.3390/app10010375 fatcat:tlcmfp54hvakjj6zzdxpds3vlu
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