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Remote Sensing
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Remote Sensing Applications of Image Denoising and Restoration
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Remote Sensing Applications of Image Denoising and Restoration

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

Deadline for manuscript submissions: closed (17 July 2021) | Viewed by 21077

Special Issue Editors

Dr. Behnood Rasti

E-Mail Website
Guest Editor
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology Chemnitzer Str. 40, 09599 Freiberg-Dresden, Germany
Interests: signal and image processing; machine learning; hyperspectral image analysis; multisensor data fusion; remote sensing
Prof. Dr. Paul Scheunders

E-Mail Website
Guest Editor
Vision Lab, University of Antwerp (CDE), Universiteitsplein 1 (N Building), B-2610 Antwerp, Belgium
Interests: remote sensing data analysis; hyperspectral image analysis; machine learning; spectral unmixing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedram Ghamisi

grade E-Mail Website
Guest Editor
1.Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Wien, Austria
Interests: hyperspectral image interpretation; multisensor and multitemporal data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in remote sensing technologies have presented a broad range of applications related to Earth observation and monitoring. Remotely sensed data are often degraded by different noise sources and artifacts that appear differently in different data acquisition technologies and sensors. The received radiance in optical remote sensing data is often degraded by mixed noise. Push-broom scanning induces striping noise in spectral imaging technologies. Light detection and ranging (LiDAR) data are degraded by impulse noises, and synthetic aperture radar (SAR) suffers from speckle noises. On the other hand, noise reduction and data restoration can improve the signal-to-noise ratio of acquired data and consequently affect remote sensing applications. However, the complexity and variety of remote sensing imaging technologies make the denoising and restoration of different data sources, from ground measurements to aerial and space measurements, very challenging. This Special Issue aims to address these problems by providing a variety of contributions focused on the application of image denoising and restoration in remote sensing data analysis. Additionally, this Special Issue promotes the use of image denoising and restoration as a preprocessing step for further remote sensing data processing. Therefore, the Special Issue’s contributions include (but are not limited to) remote sensing applications of the following topics:

  • Mixed noise reduction for hyperspectral images;
  • Despeckling for synthetic aperture radars;
  • Destriping for optical imagery;
  • Image inpainting;
  • Drone-borne sensor restoration and image denoising;
  • Advanced image processing for restoration and denoising;
  • Advanced machine learning and deep learning techniques for image restoration and denoising;
  • Artifacts removal from remote sensing data, including ground-, drone (UAV)-, aerial-, and space-based measurements;
  • Image demosaicking for remote sensing data;
  • The application of image denoising and restoration for classification, land-cover mapping, super-resolution and sharpening, unmixing, target detection, change detection, multitemporal remote sensing analysis, and data fusion.

Dr. Behnood Rasti
Prof. Dr. Paul Scheunders
Dr. Pedram Ghamisi
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 submissions that pass pre-check are 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 2700 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

  • restoration
  • denoising
  • despeckling
  • destriping
  • inpainting
  • demosaicking
  • sparse noise
  • mixed noise
  • artifact removal
  • remote sensing

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Published Papers (5 papers)

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Research

24 pages, 2874 KiB  
Article
Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm
by Wenfeng Kong, Yangyang Song and Jing Liu
Remote Sens. 2021, 13(19), 3829; https://doi.org/10.3390/rs13193829 - 24 Sep 2021
Cited by 7 | Viewed by 2316
Abstract
During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step. In HSI denoising tasks, the method based on low-rank prior has achieved [...] Read more.
During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step. In HSI denoising tasks, the method based on low-rank prior has achieved satisfying results. Among numerous denoising methods, the tensor nuclear norm (TNN), based on the tensor singular value decomposition (t-SVD), is employed to describe the low-rank prior approximately. Its calculation can be sped up by the fast Fourier transform (FFT). However, TNN is computed by the Fourier transform, which lacks the function of locating frequency. Besides, it only describes the low-rankness of the spectral correlations and ignores the spatial dimensions’ information. In this paper, to overcome the above deficiencies, we use the basis redundancy of the framelet and the low-rank characteristics of HSI in three modes. We propose the framelet-based tensor fibered rank as a new representation of the tensor rank, and the framelet-based three-modal tensor nuclear norm (F-3MTNN) as its convex relaxation. Meanwhile, the F-3MTNN is the new regularization of the denoising model. It can explore the low-rank characteristics of HSI along three modes that are more flexible and comprehensive. Moreover, we design an efficient algorithm via the alternating direction method of multipliers (ADMM). Finally, the numerical results of several experiments have shown the superior denoising performance of the proposed F-3MTNN model. Full article
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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23 pages, 6976 KiB  
Article
Accelerating Haze Removal Algorithm Using CUDA
by Xianyun Wu, Keyan Wang, Yunsong Li, Kai Liu and Bormin Huang
Remote Sens. 2021, 13(1), 85; https://doi.org/10.3390/rs13010085 - 29 Dec 2020
Cited by 5 | Viewed by 3762
Abstract
The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel [...] Read more.
The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal. Full article
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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19 pages, 3258 KiB  
Article
Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging
by Xingyu Tuo, Yin Zhang, Yulin Huang and Jianyu Yang
Remote Sens. 2020, 12(18), 2877; https://doi.org/10.3390/rs12182877 - 5 Sep 2020
Cited by 4 | Viewed by 2891
Abstract
The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence [...] Read more.
The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation. Full article
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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20 pages, 2359 KiB  
Article
How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
by Behnood Rasti, Bikram Koirala, Paul Scheunders and Pedram Ghamisi
Remote Sens. 2020, 12(11), 1728; https://doi.org/10.3390/rs12111728 - 28 May 2020
Cited by 13 | Viewed by 4020
Abstract
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in [...] Read more.
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets. Full article
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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22 pages, 22091 KiB  
Article
Nonlocal CNN SAR Image Despeckling
by Davide Cozzolino, Luisa Verdoliva, Giuseppe Scarpa and Giovanni Poggi
Remote Sens. 2020, 12(6), 1006; https://doi.org/10.3390/rs12061006 - 20 Mar 2020
Cited by 67 | Viewed by 6372
Abstract
We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest [...] Read more.
We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling. Full article
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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