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Image Restoration for Remote Sensing: Overview and Toolbox
[article]
2021
arXiv
pre-print
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This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration ...
., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. ...
[150] introduced the Huber-Markov variational model with spatially local adaptive edge-preserving ability for HSIs deblurring. From the spectral viewpoint, Cao et al. [151] and Lim et al. ...
arXiv:2107.00557v2
fatcat:adn5fpdza5h4tbsycg7yw6rqzu
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
[article]
2017
arXiv
pre-print
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Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. ...
To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and ...
HSI Super-resolution HSI super-resolution refers to the fusion of a hyperspectral image (low spatial but high spectral resolution) with a panchromatic/multispectral image (high spatial but low spectral ...
arXiv:1709.00192v1
fatcat:ueremc3lzvhlna42pgngpgb3ka
Column-Spatial Correction Network for Remote Sensing Image Destriping
2022
Remote Sensing
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Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping ...
In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. ...
[36] , in which the Huber-Markov-based variation model is used as the prior likelihood probability density function. ...
doi:10.3390/rs14143376
fatcat:uiuywcbj7fazfcqhjnndarj66y
Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint
2017
Remote Sensing
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In [32] , the authors proposed the graph-regularizer low-rank representation (LRR) for destriping of hyperspectral images. ...
In [24], Shen and Zhang proposed a maximum a posterior framework based on Huber-Markov regularization for both destriping and inpainting problems. ...
Acknowledgments: The authors would like to thank the anonymous reviewers and the Editor for their constructive comments which helped to improve the quality of the paper. ...
doi:10.3390/rs9060559
fatcat:2qyckfhadfabzhjbkqye3lcrl4
Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties
2016
IEEE Transactions on Geoscience and Remote Sensing
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Index Terms-Alternating direction method of multipliers (ADMM), destriping, optimization-based model, remote sensing image, sparsity. ...
Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. ...
More recently, low-rank matrix recovery has been used to remove stripes in hyperspectral images [20] . ...
doi:10.1109/tgrs.2015.2510418
fatcat:e42reswzujdldbgahulk4uj4hm
Missing Information Reconstruction of Remote Sensing Data: A Technical Review
2015
IEEE Geoscience and Remote Sensing Magazine
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In the past decades, missing information reconstruction of remote sensing data has become an active research field, and a large number of algorithms have been developed. ...
Because of sensor malfunction and poor atmospheric conditions, there is usually a great deal of missing information in optical remote sensing data, which reduces the usage rate and hinders the follow-up ...
In these methods, the spatial coherence is en-sured via a global optimization of the Markov random field (MRF) energy function over the entire image. ...
doi:10.1109/mgrs.2015.2441912
fatcat:vrhsm6zggjeedhtivusqshphum