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Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution

Ruituo Jiang, Xu Li, Ang Gao, Lixin Li, Hongying Meng, Shigang Yue, Lei Zhang
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
A generative adversarial network for HSIs super-resolution (HSRGAN) is proposed in this paper.  ...  Specifically, HSRGAN constructs spectral and spatial blocks with residual network in generator to effectively learn spectral and spatial features from HSIs.  ...  CONCLUSION This paper presents a novel super-resolution method for hyperspectral images which considers learning both spectral and spatial features based on generative adversarial network.  ... 
doi:10.1109/igarss.2019.8900228 dblp:conf/igarss/JiangLGLMYZ19 fatcat:tzsb6osp25bktnvn5mawswjsza

Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [article]

Junjun Jiang, He Sun, Xianming Liu, Jiayi Ma
2020 arXiv   pre-print
In this paper, we make a step forward by investigating how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches for computationally efficient single hyperspectral  ...  image super-resolution, referred as SSPSR.  ...  based hyperspectral image super-resolution (sometimes called hyperspectral image pansharpening) and single hyperspectral image super-resolution [3] .  ... 
arXiv:2005.08752v1 fatcat:pt3whdmj2fanpckezab6gf2xru

Adversarial Networks for Scale Feature-Attention Spectral Image Reconstruction from a Single RGB

Pengfei Liu, Huaici Zhao
2020 Sensors  
In this paper, we propose two advanced Generative Adversarial Networks (GAN) for the heavily underconstrained inverse problem.  ...  We establish the feature pyramid inside the network and use the attention mechanism for feature selection.  ...  Generative adversarial networks (GANs) have been vigorously studied and have been proven to be suitable for image-to-image translation tasks.  ... 
doi:10.3390/s20082426 pmid:32344686 pmcid:PMC7219499 fatcat:h4gcdz6zsjbo3bzbbj7f75ifma

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TIP 2021 5477-5489 Adversarial Multi-Path Residual Network for Image Super-Resolution.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

High-throughput molecular imaging via deep learning enabled Raman spectroscopy [article]

Conor C. Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Phillipe St-Pierre, Tom Vercauteren, Molly M. Stevens, Mads S. Bergholt
2020 arXiv   pre-print
Next, we develop a neural network for robust 2-4x super-resolution of hyperspectral Raman images that preserves molecular cellular information.  ...  Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman spectroscopy, termed DeepeR, trained on a large dataset of hyperspectral Raman images,  ...  Acknowledgements Conflict of Interest TV is founding director and shareholder of Hypervision Surgical Ltd and holds shares from Mauna Kea Technologies.  ... 
arXiv:2009.13318v1 fatcat:vgmug6rbsrdwtbvgywe4mt4rpy

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Conor C. Horgan, Magnus Jensen, Anika Nagelkerke, Jean-Philippe St-Pierre, Tom Vercauteren, Molly M. Stevens, Mads S. Bergholt
2021 Analytical Chemistry  
Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information.  ...  We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology.  ...  The authors declare the following competing financial interest(s): Tom Vercauteren is founding director and shareholder of Hypervision Surgical Ltd and holds shares from Mauna Kea Technologies.  ... 
doi:10.1021/acs.analchem.1c02178 pmid:34797972 pmcid:PMC9286315 fatcat:uj5lfsp3ojaprislhxfyxj4e2y

Learning Based Super Resolution Application for Hyperspectral Images

Hüseyin AYDİLEK, Nihat İNANÇ
2021 International scientific and vocational studies journal  
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  ...  First the application obtains a super-resolution image from a single hyperspectral image with a low spatial image with a deep convolutional neural network.  ...  Jiaa, Ji, Zhaoa, and Geng (2018) proposed a super-resolution hyperspectral image generation method using deep convolutional neural networks in their study.  ... 
doi:10.47897/bilmes.1049338 fatcat:wayekvbxkngn7mn4yhwxvocpqi

Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution [article]

Chih-Chung Hsu, Chia-Hsiang Lin
2019 arXiv   pre-print
Recently, an enhanced super-resolution based on generative adversarial network (ESRGAN) has achieved excellent performance in terms of both qualitative and quantitative quality of the reconstructed high-resolution  ...  Deep learning-based single image super-resolution enables very fast and high-visual-quality reconstruction.  ...  Acknowledgment This study was supported in part by the Ministry of Science and Technology, Taiwan, under Grants MOST 108-2634-F-007-009, 107-2218-E-020-002-MY3, and 108-2218-E-006 -052.  ... 
arXiv:1911.08711v1 fatcat:urxkgffihvekngckbzjrg6uikm

Editorial for the Special Issue on Advanced Machine Learning Techniques for Sensing and Imaging Applications

Bihan Wen, Zhangyang Wang
2022 Micromachines  
Recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in the emerging field of computational sensing and imaging [...]  ...  Mingzheng Hou [3] proposed to apply a generative adversarial network to model extremely low-resolution images that even lack adequate scene and appearance information.  ...  Recent works showed that deep learning based super-resolution algorithms can effectively restore high-resolution images from their lowresolution measurements, which can be useful for many imaging applications  ... 
doi:10.3390/mi13071030 pmid:35888847 pmcid:PMC9319337 fatcat:qmjmibo75fd6pmagaycywo3vpi

Generative Adversarial Networks for Image Super-Resolution: A Survey [article]

Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, Yanning Zhang
2022 arXiv   pre-print
Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR.  ...  Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised  ...  The use of an adversarial learning was a good tool to simultaneously address text recognition and super-resolution [116] .  ... 
arXiv:2204.13620v1 fatcat:hlwdqith65cxrbqrnbphjz6u4u

Hyperspectral Image Classification Based on Visible–Infrared Sensors and Residual Generative Adversarial Networks

Hui-Wei Su, Ri-hui Tan, Chih-Cheng Chen, Zhongzheng Hu, Avinash Shankaranarayanan
2021 Sensors and materials  
First, the generative adversarial network (GAN) is based on a dense residual network, which fully learns the higher-level features of HSIs.  ...  Hyperspectral remote sensing images have high spectral resolution and provide rich information on the types of features, but their high data dimensions and large data volume pose challenges in data processing  ...  S. was supported by a Ministry of Science and Technology grant (MOST-1102222E035002).  ... 
doi:10.18494/sam.2021.3527 fatcat:2vjgghqie5fcnatrmmarigz2mq

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Wu, and W. Song 10179 SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network ..... .......................................................................  ...  Qi 10455 Spectral-Spatial Fractal Residual Convolutional Neural Network With Data Balance Augmentation for Hyperspectral Classification .Optical Data Enhanced Facade Parsing for Street-Level Images Using  ... 
doi:10.1109/tgrs.2021.3120817 fatcat:di4236rpmfbi3cb2gypvqtzlzi

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Li 5028 Adaptive DropBlock-Enhanced Generative Adversarial Networks for Hyperspectral Image Classification .............. ...............................................................................  ...  Zhan, and S. Chen 4654 Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks .......................... .......................................................... P.  ... 
doi:10.1109/tgrs.2021.3075585 fatcat:467tqoqtufdf5lbdit6xq554na

Residual Pixel Attention Network for Spectral Reconstruction from RGB Images

Hao Peng, Xiaomei Chen, Jie Zhao
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this paper, we proposed a convolution neural network of the hyperspectral reconstruction from a single RGB image, called Residual Pixel Attention Network (RPAN).  ...  In recent years, hyperspectral reconstruction based on RGB imaging has made significant progress of deep learning, which greatly improves the accuracy of the reconstructed hyperspectral images.  ...  [2] proposed a method based on generative adversarial network (GAN) to achieve hyperspectral image reconstruction.  ... 
doi:10.1109/cvprw50498.2020.00251 dblp:conf/cvpr/PengCZ20 fatcat:3twon4nqkbg2zmou6boywg7f3a

Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning [article]

Marc Bosch and Christopher M. Gifford and Pedro A. Rodriguez
2017 arXiv   pre-print
Recent advances in Generative Adversarial Learning allow for new modalities of image super-resolution by learning low to high resolution mappings.  ...  In this paper we present our work using Generative Adversarial Networks (GANs) with applications to overhead and satellite imagery. We have experimented with several state-of-the-art architectures.  ...  Generative Adversarial Networks (GANs) are one of the most popular generative Deep Learning framework for super-resolution.  ... 
arXiv:1711.10312v1 fatcat:6pl6vuwkvvgnxafjiqxp7ia5s4
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