☆ 4.7 Article

A Group-Based Embedding Learning and Integration Network for Hyperspectral Image Super-Resolution

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Automation & Control Systems

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Xinya Wang et al.

Summary: Previous deep learning-based super-resolution methods rely on predefined degradation processes and may suffer from deterioration when the real degradation is inconsistent. In this paper, we propose a contrastive regularization method that exploits blurry and clear images as negative and positive samples, respectively, to improve blind super-resolution performance. We also extract global statistical prior information instead of estimating degradation to capture the distortion characteristics and make our method adaptive to changes in distortions. Experimental results demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2023)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and SpatialSpectral Consistency Regularization

Xinya Wang et al.

Summary: In this article, a novel single hyperspectral image super-resolution (SR) method called RFSR is proposed. The method models the spectrum correlations from a sequence perspective and utilizes a recurrent feedback network to fully exploit the information among the spectra of the hyperspectral data. Experimental results demonstrate the advantage of the proposed approach over the state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Hyperspectral image super-resolution via multi-domain feature learning

Qiang Li et al.

Summary: In this study, a multi-domain feature learning network using 2D/3D convolution for hyperspectral image super-resolution is proposed. By sharing spatial information and fusing multi-domain features, the spatial reconstruction and spectral fidelity are improved. Moreover, an edge generation mechanism is introduced to recover more edge details.

NEUROCOMPUTING (2022)

添加到收藏夹

Article Automation & Control Systems

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

Jiayi Ma et al.

Summary: This study proposes a novel image fusion framework called SwinFusion, which combines cross-domain long-range learning and Swin Transformer. The framework integrates complementary information and achieves global interaction through attention-guided cross-domain modules. It also addresses multi-scene image fusion problems by preserving structure, detail, and intensity. Extensive experiments prove the superiority of SwinFusion compared to other state-of-the-art fusion algorithms. The implementation code and pre-trained weights are available at https://github.com/Linfeng-Tang/SwinFusion.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2022)

添加到收藏夹

Article Automation & Control Systems

Hyperspectral Image Superresolution Using Spectrum and Feature Context

Qi Wang et al.

Summary: This study proposes a new hyperspectral image superresolution method, utilizing a dual-channel network designed through 2D and 3D convolution to jointly exploit information from single and adjacent bands, while introducing feature context fusion to significantly enhance algorithm performance.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

添加到收藏夹

Article Geochemistry & Geophysics

A Spectral Grouping and Attention-Driven Residual Dense Network for Hyperspectral Image Super-Resolution

Denghong Liu et al.

Summary: A novel CNN-based method, SGARDN, is proposed in this article for hyperspectral image super-resolution, utilizing spectral grouping and attention mechanisms to extract effective spatial-spectral features and improve feature expression and spectral correlation learning. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art methods in synthesized and real-scenario hyperspectral images.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution

Kelvin C. K. Chan et al.

Summary: In this study, we introduce a method called GLEAN, which utilizes pre-trained GAN as a latent bank to enhance the restoration quality of large-factor image super-resolution. Unlike existing SR methods, GLEAN leverages rich and diverse priors from GAN directly to generate upscaled images with a single forward pass.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Hyperspectral Anomaly Detection With Otsu-Based Isolation Forest

Yuxiang Zhang et al.

Summary: The proposed method in this article, based on an Otsu-based isolation forest, effectively separates anomalies from backgrounds by assembling multiple binary trees and using the Otsu-based splitting criterion for a more discriminative binary tree construction.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

添加到收藏夹

Article Environmental Sciences

Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution

Qiang Li et al.

REMOTE SENSING (2020)

添加到收藏夹

Article Geochemistry & Geophysics

Semisupervised Classification Based on SLIC Segmentation for Hyperspectral Image

Yuxiang Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery

Junjun Jiang et al.

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING (2020)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Image Super-Resolution Using Deep Feature Matrix Factorization

Weiying Xie et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net

Qi Xie et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A MAP-Based Approach for Hyperspectral Imagery Super-Resolution

Hasan Irmak et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization

Shutao Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Image Super-Resolution by Spectral Difference Learning and Spatial Error Correction

Jing Hu et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2017)

添加到收藏夹

Article Engineering, Electrical & Electronic

Hyperspectral Image Superresolution by Transfer Learning

Yuan Yuan et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)

添加到收藏夹

Review Biochemical Research Methods

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Amy Lowe et al.

PLANT METHODS (2017)

添加到收藏夹

Article Environmental Sciences

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Yao Wang et al.

REMOTE SENSING (2017)

添加到收藏夹

Article Environmental Sciences

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

Shaohui Mei et al.

REMOTE SENSING (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Wei-Sheng Lai et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Christian Ledig et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial-Spectral Group Sparsity

Jie Li et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Sparse Recovery of Hyperspectral Signal from Natural RGB Images

Boaz Arad et al.

COMPUTER VISION - ECCV 2016, PT VII (2016)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation

Qi Wei et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2015)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Bayesian Sparse Representation for Hyperspectral Image Super Resolution

Naveed Akhtar et al.

2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2015)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection

Dongyoon Han et al.

2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2015)

添加到收藏夹

Review Biochemical Research Methods

Medical hyperspectral imaging: a review

Guolan Lu et al.

JOURNAL OF BIOMEDICAL OPTICS (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum

Fumihito Yasuma et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2010)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Super-resolution reconstruction of hyperspectral images

T Akgun et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2005)

添加到收藏夹

Article Engineering, Electrical & Electronic

Super-resolution image reconstruction: A technical overview

SC Park et al.

IEEE SIGNAL PROCESSING MAGAZINE (2003)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.