Special Issue "Remote Sensing Image Denoising, Restoration and Reconstruction"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Prof. Dr. Karen Egiazarian
E-Mail Website
Guest Editor
Tampere University; Korkeakoulunkatu 1, 33720 Tampere, Finland
Interests: computational imaging; compressed sensing; efficient signal processing algorithms; image/video restoration and compression
Special Issues and Collections in MDPI journals
Prof. Dr. Aleksandra Pizurica
E-Mail Website
Guest Editor
Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
Interests: statistical image modeling; sparse representation; image restoration and reconstruction; analysis of high-dimensional data; machine learning
Special Issues and Collections in MDPI journals
Prof. Dr. Vladimir Lukin
E-Mail
Guest Editor
Department of Information-Communication Technologies, National Aerospace University, Chkalova Str., 61070 Kharkov, Ukraine
Interests: multichannel remote sensing; image processing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

During image acquisition process remote sensing images are corrupted by various kinds of degradations, such as noise, geometric distortions, changes in illumination, blur (motion, atmospheric turbulence, out-of-focus), etc. Image restoration/reconstruction (IR) is an inverse imaging problem aiming at estimating original images from the observed distorted ones. IR can be applied on a sensor data at the pre-processing stage, to improve image quality and to support further stages of data analysis, object detection and classification, or at the post-processing stage, to reduce distortions caused by lossless coding of images (blocking and ringing artifacts).

This Special Issue will present recent advances in inverse imaging of remote sensing data. Specifically, novel model-based, machine learning methods, or hybrid methods of image restoration, image denoising, deblurring (blind and non-blind), image super-resolution will be of special attention.

Topics of interest include but are not limited to:

  • Image denoising
  • Image deblurring (blind and non-blind)
  • Image super-resolution
  • Image dehazing and de-raining
  • Image compression artifacts reduction
  • The effect of image restoration on clustering, classification and target detection
  • Sparse representation and low-rank approximation for image restoration in remote sensing
  • Deep learning models for image restoration, with emphasis on robustness to adversarial attacks and data variation
  • Multimodal image restoration and joint restoration and fusion of multi-sensor data

Prof. Dr. Karen Egiazarian
Prof. Dr. Aleksandra Pizurica
Prof. Dr. Vladimir Lukin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Image denoising and enhancement
  • Image deblurring (blind and non-blind)
  • Image super-resolution
  • Image dehazing and de-raining
  • Image compression artifacts reduction
  • Restoration of multi-modal images and multi-sensor data

Published Papers (5 papers)

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Research

Article
Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data
Remote Sens. 2021, 13(18), 3671; https://doi.org/10.3390/rs13183671 - 14 Sep 2021
Viewed by 432
Abstract
This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, [...] Read more.
This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor L-Singular Value Decomposition (L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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Article
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks
Remote Sens. 2021, 13(16), 3167; https://doi.org/10.3390/rs13163167 - 10 Aug 2021
Viewed by 374
Abstract
Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images [...] Read more.
Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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Article
Mid-Infrared Compressive Hyperspectral Imaging
Remote Sens. 2021, 13(4), 741; https://doi.org/10.3390/rs13040741 - 17 Feb 2021
Viewed by 683
Abstract
Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to [...] Read more.
Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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Article
Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method
Remote Sens. 2021, 13(4), 549; https://doi.org/10.3390/rs13040549 - 04 Feb 2021
Cited by 1 | Viewed by 709
Abstract
Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3) [...] Read more.
Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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Article
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization
Remote Sens. 2020, 12(20), 3446; https://doi.org/10.3390/rs12203446 - 20 Oct 2020
Cited by 4 | Viewed by 784
Abstract
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow [...] Read more.
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow as well as recover the cloud-contaminated pixels. Generally, the thick cloud and cloud shadow element are not only sparse but also smooth along the spatial horizontal and vertical direction, while the clean element is smooth along the temporal direction. Guided by the above insight, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity regularized tensor optimization (TSSTO) is proposed in this paper. Firstly, the sparsity norm is utilized to boost the sparsity of the cloud and cloud shadow element, and unidirectional total variation (UTV) regularizers are applied to ensure the smoothness in different directions. Then, through thresholding, the cloud mask and the cloud shadow mask can be acquired and used to guide the substitution. Finally, the reference image is selected to reconstruct details of the repairing area. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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