Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network

Sun, Hai-Han; Lee, Yee Hui; Dai, Qiqi; Li, Chongyi; Ow, Genevieve; Yusof, Mohamed Lokman Mohd; Yücel, Abdulkadir C.

doi:10.1109/tgrs.2021.3138974

Cited by 15 publications

(9 citation statements)

References 54 publications

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“…They have exemplified the remarkable effectiveness of deep learning in addressing a wide array of EM inverse problems. The utility of deep learning-based methods has also extended to GPR applications [127,128], with a particular emphasis on resolving GPR inverse problems, including image classification, signature recognition, subsurface object detection, and the restoration of object properties [129][130][131][132][133]. Within this context, Deep Neural Networks (DNNs) have been harnessed to reconstruct 2D subsurface permittivity maps based on GPR B-scans [134][135][136][137].…”

Section: A Deep Learning-based 3d Ground-penetrating Radar Data Inver...mentioning

confidence: 99%

Algorithms in Tomography and Related Inverse Problems—A Review

Tassiopoulou,

Koukiou,

Anastassopoulos

2024

Algorithms

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In the ever-evolving landscape of tomographic imaging algorithms, this literature review explores a diverse array of themes shaping the field’s progress. It encompasses foundational principles, special innovative approaches, tomographic implementation algorithms, and applications of tomography in medicine, natural sciences, remote sensing, and seismology. This choice is to show off the diversity of tomographic applications and simultaneously the new trends in tomography in recent years. Accordingly, the evaluation of backprojection methods for breast tomographic reconstruction is highlighted. After that, multi-slice fusion takes center stage, promising real-time insights into dynamic processes and advanced diagnosis. Computational efficiency, especially in methods for accelerating tomographic reconstruction algorithms on commodity PC graphics hardware, is also presented. In geophysics, a deep learning-based approach to ground-penetrating radar (GPR) data inversion propels us into the future of geological and environmental sciences. We venture into Earth sciences with global seismic tomography: the inverse problem and beyond, understanding the Earth’s subsurface through advanced inverse problem solutions and pushing boundaries. Lastly, optical coherence tomography is reviewed in basic applications for revealing tiny biological tissue structures. This review presents the main categories of applications of tomography, providing a deep insight into the methods and algorithms that have been developed so far so that the reader who wants to deal with the subject is fully informed.

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Section: A Deep Learning-based 3d Ground-penetrating Radar Data Inver...mentioning

confidence: 99%

Algorithms in Tomography and Related Inverse Problems—A Review

Tassiopoulou,

Koukiou,

Anastassopoulos

2024

Algorithms

View full text Add to dashboard Cite

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“…As artificial intelligence technologies continue to evolve, the associated industries are also changing, and thus the requirements for related downstream tasks are increasing. The quality of tasks completed in the image processing area [ 1 , 2 , 3 , 4 , 5 ] can greatly affect the efficiency of upstream tasks.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning-Based Low-Light Image Enhancement

Tian,

Qu,

et al. 2023

Sensors

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Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancement based on deep learning in four aspects. First, we introduce the related methods of low-light image enhancement based on deep learning. We then describe the low-light image quality evaluation methods, organize the low-light image dataset, and finally compare and analyze the advantages and disadvantages of the related methods and give an outlook on the future development direction.

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“…With the strong feature representation and learning capability, deep learning-based methods have been involved in solving challenging GPR tasks, such as target detection [34], [35], characterization [36]- [38], and inverse imaging [39], [40]. There is a rising trend to apply neural networks in the clutter removal task.…”

Section: Introductionmentioning

confidence: 99%

Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

Sun,

Cheng,

Fan

2022

Preprint

Self Cite

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The clutter in the ground-penetrating radar (GPR) radargram disguises or distorts subsurface target responses, which severely affects the accuracy of target detection and identification. Existing clutter removal methods either leave residual clutter or deform target responses when facing complex and irregular clutter in the real-world radargram. To tackle the challenge of clutter removal in real scenarios, a clutter-removal neural network (CR-Net) trained on a large-scale hybrid dataset is presented in this study. The CR-Net integrates residual dense blocks into the U-Net architecture to enhance its capability in clutter suppression and target reflection restoration. The combination of the mean absolute error (MAE) loss and the multiscale structural similarity (MS-SSIM) loss is used to effectively drive the optimization of the network. To train the proposed CR-Net to remove complex and diverse clutter in real-world radargrams, the first large-scale hybrid dataset named CLT-GPR dataset containing clutter collected by different GPR systems in multiple scenarios is built. The CLT-GPR dataset significantly improves the generalizability of the network to remove clutter in real-world GPR radargrams. Extensive experimental results demonstrate that the CR-Net achieves superior performance over existing methods in removing clutter and restoring target responses in diverse real-world scenarios. Moreover, the CR-Net with its end-to-end design does not require manual parameter tuning, making it highly suitable for automatically producing clutter-free radargrams in GPR applications. The CLT-GPR dataset and the code implemented in the paper can be found at https://haihan-sun.github.io/GPR.html.

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Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network

Cited by 15 publications

References 54 publications

Algorithms in Tomography and Related Inverse Problems—A Review

Algorithms in Tomography and Related Inverse Problems—A Review

A Survey of Deep Learning-Based Low-Light Image Enhancement

Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data

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