Special Issue "Entropy-Based Algorithms for Signal Processing"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Prof. Dr. Gwanggil Jeon
E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Korea
Tel. +82-32-835-8946
Interests: image/signal processing; entropy coding; artificial intelligence; color image processing; machine learning; remote sensing; hyperspectral imaging; data fusion Learning; Remote Sensing; Hyperspectral Imaging; Data Fusion
Special Issues and Collections in MDPI journals
Prof. Abdellah Chehri
E-Mail Website
Guest Editor
Département des Sciences Appliquées, Université de Québec à Chicoutimi, 555, boul. de l’Université, Chicoutimi, Québec, Canada, G7H 2B1
Interests: Entropy Coding; Remote Sensing; Biomedical Analysis of Signals; Pattern Recognition; Image Processing, Signal Processing

Special Issue Information

Dear Colleagues,

Entropy, the key factor of information theory, is one of the most important research areas in computer science. Entropy coding informs us of the formal limits of today’s storage and communication infrastructure. Over the last few years, entropy has become as an adequate trade-off measure in signal processing. Entropy measures especially have been used in image and video processing by applying sparsity and are able to help us to solve several of the issues that we are currently facing. As the daily produced data are increasing rapidly, a more effective approach to encode or compress the big data is required. In this sense, applications of entropy coding can improve image and video coding, remote sensing imaging, quality assessment in agricultural products, and product inspection, by applying more effective coding approaches. This Special Issue calls for recent studies on various signal processing approaches that are based on entropy coding. Papers of both a theoretical and applicative nature are welcome, as well as contributions regarding new image and video processing techniques for the entropy research community. Major, though by no means exclusive, topics of interest are:

Keywords:

  • Multichannel imaging;
  • Sensor size, channel number, dynamic range;
  • Modeling of signal processing;
  • Compression approach;
  • Entropy-based video coding;
  • Prediction and redundancy for video coding;
  • Noise removal approach;
  • GPU-based methods for signal processing;
  • Signal quality assessment.

Prof. Gwanggil Jeon
Prof. Abdellah Chehri
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. Entropy is an international peer-reviewed open access monthly 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 1600 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 (3 papers)

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Research

Open AccessArticle
A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy
Entropy 2019, 21(12), 1135; https://doi.org/10.3390/e21121135 - 21 Nov 2019
Abstract
In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high- and low-frequency components, the corresponding decomposed components are not precise enough [...] Read more.
In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high- and low-frequency components, the corresponding decomposed components are not precise enough for the following infrared-visible fusion operations. This paper proposes a non-subsampled contourlet transform (NSCT) based decomposition method for image fusion, by which source images are decomposed to obtain corresponding high- and low-frequency sub-bands. Unlike MST, the obtained high-frequency sub-bands have different decomposition layers, and each layer contains different information. In order to obtain a more informative fused high-frequency component, maximum absolute value and pulse coupled neural network (PCNN) fusion rules are applied to different sub-bands of high-frequency components. Activity measures, such as phase congruency (PC), local measure of sharpness change (LSCM), and local signal strength (LSS), are designed to enhance the detailed features of fused low-frequency components. The fused high- and low-frequency components are integrated to form a fused image. The experiment results show that the fused images obtained by the proposed method achieve good performance in clarity, contrast, and image information entropy. Full article
(This article belongs to the Special Issue Entropy-Based Algorithms for Signal Processing)
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Open AccessArticle
Recognition of a Single Dynamic Gesture with the Segmentation Technique HS-ab and Principle Components Analysis (PCA)
Entropy 2019, 21(11), 1114; https://doi.org/10.3390/e21111114 - 14 Nov 2019
Abstract
A continuous path performed by the hand in a period of time is considered for the purpose of gesture recognition. Dynamic gestures recognition is a complex topic since it spans from the conventional method of separating the hand from surrounding environment to searching [...] Read more.
A continuous path performed by the hand in a period of time is considered for the purpose of gesture recognition. Dynamic gestures recognition is a complex topic since it spans from the conventional method of separating the hand from surrounding environment to searching for the fingers and palm. This paper proposes a strategy of hand recognition using a PC webcam, a segmentation technique (HS-ab which means HSV and CIELab color space), pre-processing of images to reduce noise and a classifier such as Principle Components Analysis (PCA) for the detection and tracking of the hand of the user. The results show that the segmentation technique HS-ab and the method PCA are robust in the execution of the system, although there are various conditions such as illumination, speed and precision of the movements. It is for this reason that a suitable extraction and classification of features allows the location of the gesture. The system was tested with the database of the training images and has a 94.74% accuracy. Full article
(This article belongs to the Special Issue Entropy-Based Algorithms for Signal Processing)
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Open AccessArticle
An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery
Entropy 2019, 21(9), 900; https://doi.org/10.3390/e21090900 - 17 Sep 2019
Abstract
Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, to seek a domain where a signal can exhibit a high degree of sparsity and to design an effective algorithm have [...] Read more.
Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, to seek a domain where a signal can exhibit a high degree of sparsity and to design an effective algorithm have drawn increasingly more attention. Among various sparsity-based models, structured or group sparsity often leads to more powerful signal reconstruction techniques. In this paper, we propose a novel entropy-based algorithm for CS recovery to enhance image sparsity through learning the group sparsity of residual. To reduce the residual of similar packed patches, the group sparsity of residual is described by a Laplacian scale mixture (LSM) model, therefore, each singular value of the residual of similar packed patches is modeled as a Laplacian distribution with a variable scale parameter, to exploit the benefits of high-order dependency among sparse coefficients. Due to the latent variables, the maximum a posteriori (MAP) estimation of the sparse coefficients cannot be obtained, thus, we design a loss function for expectation–maximization (EM) method based on relative entropy. In the frame of EM iteration, the sparse coefficients can be estimated with the denoising-based approximate message passing (D-AMP) algorithm. Experimental results have shown that the proposed algorithm can significantly outperform existing CS techniques for image recovery. Full article
(This article belongs to the Special Issue Entropy-Based Algorithms for Signal Processing)
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