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Applied Sciences
Special Issues
Structured Sparse Signal Processing for Infrared and Terahertz Systems
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Structured Sparse Signal Processing for Infrared and Terahertz Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 2784

Special Issue Editors

Dr. Lu Gan

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Science, Brunel University, London, UK
Interests: Signal processing; machine learning; terahertz imaging; wavelets; high dynamic signal sampling
Prof. Dr. Yaochun Shen

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L3 5TR, UK
Interests: optical coherence tomography; terahertz imaging; non-destructive testing; imaging method; process analytical technology; coating
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrared and terahertz (THz) systems hold tremendous potential for applications in robotics, medicine, non-destructive inspection, quality control, and homeland security, etc. These sensing systems often generate high-dimensional signals with large volume. The recent explosive growth of sparse signal processing and machine learning offer new opportunities and tools to design high performance sensing systems.

The goal of the Special Issue is to collect original and high-quality research articles as well as review papers focused on recent advances of structured sparse signal processing and deep learning for the infrared and terahertz system. It aims to highlight new research accomplishments and developments in system design, theory, algorithms, and applications. This Special Issue will include high-quality novel contributions in this emerging field, including, but not limited to:

  • Infrared spectroscopy and imaging systems;
  • Terahertz spectroscopy and imaging systems;
  • Non-destructive testing;
  • Sparse signal processing;
  • Structured sparsity learning;
  • Compressive sensing;
  • Computational imaging;
  • Terahertz and infrared spectroscopic imaging.

Dr. Lu Gan
Prof. Dr. Yaochun Shen
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. Applied Sciences 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.

Published Papers (2 papers)

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Research

17 pages, 2929 KiB  
Article
DRUNet: A Method for Infrared Point Target Detection
by Changan Wei, Qiqi Li, Ji Xu, Jingli Yang and Shouda Jiang
Appl. Sci. 2022, 12(18), 9299; https://doi.org/10.3390/app12189299 - 16 Sep 2022
Cited by 2 | Viewed by 1408
Abstract
Deep learning is widely used in vision tasks, but feature extraction of IR small targets is difficult due to the inconspicuous contours and lack of color information. This paper proposes a new convolutional neural network–based (CNN-based) method for IR small target detection called [...] Read more.
Deep learning is widely used in vision tasks, but feature extraction of IR small targets is difficult due to the inconspicuous contours and lack of color information. This paper proposes a new convolutional neural network–based (CNN-based) method for IR small target detection called DRUNet. The algorithm is divided into two parts: the feature extraction network and the prediction head. For the small IR targets, which are difficult to accurately label, Gaussian soft labels are added to supervise the training process and make the network converge faster. We use a simplified object keypoint similarity to evaluate the network accuracy by the ratio of the distance to the radius of the inner tangent circle of the target box and a fair method for evaluating the model inference speed after GPU preheating. The experimental results show that our proposed algorithm performs better when compared with commonly used algorithms in the field of small target detection. The model size is 10.5 M, and the test speed reaches 133 FPS under the RTX3090 experimental platform. Full article
(This article belongs to the Special Issue Structured Sparse Signal Processing for Infrared and Terahertz Systems)
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23 pages, 5747 KiB  
Article
Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm
by Xiangsuo Fan, Anqing Wu, Huajin Chen, Qingnan Huang and Zhiyong Xu
Appl. Sci. 2022, 12(11), 5570; https://doi.org/10.3390/app12115570 - 30 May 2022
Viewed by 1115
Abstract
In the face of complex scenes with strong edge contours and high levels of noise, suppressing edge contours and noise levels is challenging with infrared dim and small target detection algorithms. Many advanced algorithms suffer from high false alarm rates when facing this [...] Read more.
In the face of complex scenes with strong edge contours and high levels of noise, suppressing edge contours and noise levels is challenging with infrared dim and small target detection algorithms. Many advanced algorithms suffer from high false alarm rates when facing this problem. To solve this, a new anisotropic background feature weight function based on the infrared patch tensor (IPT) model was developed in this study to characterize the background airspace difference features by effectively combining the local features with the global features to suppress the strong edge contours in the structural tensor. Secondly, to enhance the target energy in the a priori model, an improved high-order cumulative model was proposed to establish the local significance region of the target as a way to achieve energy enhancement of the significant target in the structural tensor. Finally, the energy-enhanced structural tensor was introduced into the partial sum of the sensor nuclear norm (PSTNN) model as a local feature information weight matrix; the detection results were obtained by solving the model with the help of ADMM. A series of experiments show that the algorithm in this paper achieves better detection results compared with other algorithms. Full article
(This article belongs to the Special Issue Structured Sparse Signal Processing for Infrared and Terahertz Systems)
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