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Electronics
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Novel Technologies on Image and Signal Processing
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Novel Technologies on Image and Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 13652

Special Issue Editor

Dr. Bhanu Prakash KN

E-Mail Website
Guest Editor
Singapore Bioimaging Consortium (SBIC), Agency for Science, Technology and Research (A*STAR), Singapore 138667, Singapore
Interests: algorithm development for multimodal image processing; pattern recognition; machine and deep learning; statistical analysis

Special Issue Information

Dear Colleagues,

The rise of AI has made way to modern signal and image processing techniques, a paradigm shift from traditional model-based processing. These techniques have found acceptance in almost all the research areas leading to advances in computational intelligence, computing hardware (GPUs), dedicated architectures for deep learning, novel techniques of signal and image reconstruction, computer vision, healthcare, medical imaging, etc. Additionally, these techniques have empowered a new generation of algorithms to address problems with hybrid data, big data, multi-modal images and signals, efficient usage of computational resources, scalability, split and distributed learning, etc.  Hence, it is important to explore and assimilate recent trends in these research areas available around the world and disseminate the knowledge to the scientific world for better adoption and further development.  

This Special Issue aims to compile the best work in the area of “Novel technologies in signal & image processing” that underscore new methods for analysing complex images and signals, multimodality systems, classification, regression, and quantification.

Application areas include, but are not limited to:

  • Healthcare—Imaging, Decision Support Systems, Homecare, Telehealth, etc.
  • Sensors—Monitoring, wearables
  • Modelling
  • Smart living
  • Biophotonics for medicine

We invite submissions concerning the development of novel algorithms and technologies for image and signal processing. Survey papers and reviews are also welcome.

Dr. Bhanu Prakash KN
Guest Editor

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. Electronics 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

  • Signal and Image Processing
  • Artificial Intelligence
  • Healthcare
  • Wearables and IoTs
  • Computer Vision

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

19 pages, 2688 KiB  
Article
IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning
by Jing Li, Xiao Wei, Fengpin Wang and Jinjia Wang
Electronics 2021, 10(23), 3021; https://doi.org/10.3390/electronics10233021 - 3 Dec 2021
Cited by 3 | Viewed by 1882
Abstract
Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a [...] Read more.
Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM). Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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15 pages, 3614 KiB  
Article
Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
by Sundar Santhoshkumar, Vijayakumar Varadarajan, S. Gavaskar, J. Jegathesh Amalraj and A. Sumathi
Electronics 2021, 10(21), 2574; https://doi.org/10.3390/electronics10212574 - 21 Oct 2021
Cited by 19 | Viewed by 2872
Abstract
Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful [...] Read more.
Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%. Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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19 pages, 3811 KiB  
Article
Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation
by Toufique A. Soomro, Ahmed Ali, Nisar Ahmed Jandan, Ahmed J. Afifi, Muhammad Irfan, Samar Alqhtani, Adam Glowacz, Ali Alqahtani, Ryszard Tadeusiewicz, Eliasz Kantoch and Lihong Zheng
Electronics 2021, 10(18), 2297; https://doi.org/10.3390/electronics10182297 - 18 Sep 2021
Cited by 10 | Viewed by 3961
Abstract
Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. [...] Read more.
Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease. Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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13 pages, 21749 KiB  
Article
Discrete Pseudo-Fractional Fourier Transform and Its Fast Algorithm
by Dorota Majorkowska-Mech and Aleksandr Cariow
Electronics 2021, 10(17), 2145; https://doi.org/10.3390/electronics10172145 - 3 Sep 2021
Viewed by 2180
Abstract
In this article, we introduce a new discrete fractional transform for data sequences whose size is a composite number. The main kernels of the introduced transform are small-size discrete fractional Fourier transforms. Since the introduced transformation is not, in the generally known sense, [...] Read more.
In this article, we introduce a new discrete fractional transform for data sequences whose size is a composite number. The main kernels of the introduced transform are small-size discrete fractional Fourier transforms. Since the introduced transformation is not, in the generally known sense, a classical discrete fractional transform, we call it discrete pseudo-fractional Fourier transform. We also provide a generalization of this new transform, which depends on many fractional parameters. A fast algorithm for computing the introduced transform is developed and described. Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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11 pages, 1733 KiB  
Article
Frequency Estimation from Compressed Measurements of a Sinusoid in Moving-Average Colored Noise
by Nuha A. S. Alwan and Zahir M. Hussain
Electronics 2021, 10(15), 1852; https://doi.org/10.3390/electronics10151852 - 31 Jul 2021
Cited by 2 | Viewed by 1846
Abstract
Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two [...] Read more.
Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks. Full article
(This article belongs to the Special Issue Novel Technologies on Image and Signal Processing)
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