Special lssues
Table of Content

Advances and Applications in Signal, Image and Video Processing

Submission Deadline: 31 January 2024 (closed)

Guest Editors

Prof. Manoj Gupta, VIT-AP University, India.
Dr. Kashif Nisar, Victorian Institute of Technology (VIT), Australia.
Dr. Aziz Nanthaamornphong, Prince of Songkla University, Thailand.

Summary

Signal, video and image processing constitutes the basis of communications systems. With the proliferation of portable/implantable devices, embedded signal processing became widely used, despite that most of the common users are not aware of this issue, New signal, image and video processing algorithms and methods, in the context of a growing-wide range of domains (communications, medicine, finance, education, etc.) have been proposed, developed and deployed. Moreover, since the implementation platforms experience an exponential growth in terms of their performance, many signal processing techniques are reconsidered and adapted in the framework of new applications.

 

This Special issue covers the advances and application of Signal, Image and Video Processing. It will discuss integrating the principles of computer science, life science, healthcare, medical and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. This SI will cover the practical advances and applications of advances and application of Signal, Image and Video Processing. This SI will be a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the advances and application of Signal, Image and Video Processing.

 

The topics to be discussed in this special issue but are not limited to:

Architectures and frameworks   

Signal processing architectures; Hardware and software for signal, image and video processing; Bio-signal acquisition, transmission, processing, and analysis; Compressive sensing (sparse sampling); Deep learning (graphical inference algorithms); High speed video acquisition, architecture and processing; Real-time signal architectures for image and video processing; Distributed information processing, transmission and storage over networks; High speed signal processing and architectures; Big image processing and analytics in multimedia tools and applications

 

Signal processing theory and methods

Embedded signal processing; Signal and information processing on graphs; Audio and acoustic signal processing; Digital and multirate signal processing; Image and video processing; Multi-camera imaging; Speech and language processing; Multimedia signal processing; Signal processing for communications; Statistical signal processing; Sensor array and multi-channel signal processing; Machine learning, feature detection, bio-inspired techniques; Pattern recognition; Social learning models, Bayesian signal processing; Cognitive information processing; Bio-metric analysis, forensic analysis watermarking and security; Processing signal, image and video collections; Implementation of signal, image and video processing systems

High speed signal processing: integrating 5G and IoT with satellite networks.

 

mmWave communications; Very high speed camera; High speed flying sensors; 6G - integrating 5G with satellite networks; Ultra-fast access signal processing; Satellite-to-satellite communications; Sea-to-space communications; Point-to-point wireless communication networks; Super-fast broadband signals; High speed optical broadcasting fibers lines; High data rates; Ultra fast access of Internet; Full home automation and home applications; Smart homes and cities; Global energy sources; Space and defense 6G-oriented technologies; Integrated satellite-to-satellite communications; Monitoring/driving natural calamities; Sea-to-Space communications; Nano antennas; Point-to-point wireless communication networks; Space roaming; Earth observation (backhaul link); Space observation (backhaul link); Small cells (cover zones without Internet); Space-IoT (monitoring sensors); Standard adaptation to 6G for handoff and roaming challenges (GPS, Galileo, COMPASS, GLONASS).

 

Agricultural signal applications

Agriculture systems; Sensing and monitoring ecological agriculture; Food security (Food safety, Traceability, Food value chain); Bioinformatics (Agricultural data, Livestock genetics, Big Data, etc.); Bio-sensing (Soil, Atmosphere, Culture monitoring, Livestock environment); Plasma, Micro Nano Bubble application to Agriculture; Field robotics (Drones, Field labor robotics, Robots-driven livestock care).

 

Signal/image metrics

Aesthetic quality assessment; Visual aesthetic; Image quality assessment; Blind noise assessment; Perceptual evaluation; Automatic segmentation; Perceptual video compression; Perceptual transparency; Energy-based segmentation; Ultrasound image segmentation; Photon-limited imaging; Processing demosaiced images; Image restoration; Image colorization; Image denoising; Image deconvolution; Random sampling; Semantic segmentation; Sparse imaging; Dense image arrays; Distortion visibility; Depth upsampling; Infrared, multispectral and hyperspectral imaging; Image inpainting; Patch matching; Image Stitching

 

Features and models for images/signals

3D Quality assessment models; Adaptive smoothing; Background subtraction; Probabilistic illumination models; Cumulative orientation features; Efficient video saliency; Deep convolutional networks; Edge-aware filters; Prediction inaccuracy modeling; Temporal predictions; Interactive multiview videos; Video authentication; Multi-modal images; Image mosaicking; Image fusion; Low-contrast images; Adaptive pixel transfer; Omnidirectional imaging; Superpixels and adaptive superpixels; Unsupervised segmentation; Edge detection; Morphological profiles.

 

Image/signal computation and services

Computational imaging; Biological imaging; Visual saliency; Computational photography; Perceptual metrics; 3D Data analysis; Visual content analysis; Video retrieval; Video conferences; Healthcare exercising signals; 3D Virtual views; Measuring seismic images; Landmark detection; Aerial images; Motion recognition; Object tracking; Crowd scenes; Satellite images; Visual media compression; Adaptive quantization; SDN for videos; Scene analysis; Scene recognition; Anomaly detection; High quality streaming media; Detecting urban roads; Action recognition; Lenses; Plenoptic cameras; Curvature detection; Normalized cuts and geodesics;

 

Special signal, image and video processing applications/domains

Big data collection, retrieval, analysis; Cyber-physical systems; Energy and smart Grid; Monitoring and control systems; Wireless and Internet of Things; Sensors and Body networks; Healthcare and citizen wellbeing; Transportation systems; 5G communications Networks and mm-Wave Systems; Cognitive radio networks; Cyber-physical security; Social networks and finance applications, etc.

 

Hands-free speech communication and microphone arrays

Acoustic scene analysis; Acoustic event detection; Echo cancellation; Noise suppression; Hearing aids; Microphone array technology and architectures; Source localization and separation; Spatial audio for immersive environments; Speech acquisition; Speech and speaker recognition technology; Speech and audio quality assessment; Privacy of speech communications, etc.

 

Wearable sensor signal processing

Signal pre-processing algorithms; Physiological parameter monitoring; Noise reduction; Feature extraction and classification; Off-line bio-signal processing; Real-time processing of sensor signals; Bio-mechanical feedback for diagnosis and rehabilitation; Static and dynamic calibration; Signal processing to improve sensor precision and accuracy; Time-efficient and power-efficient algorithms for big data; Sensor data fusion and integration; Body-area sensor networks: real-time communication and synchronization; Gesture and movement pattern recognition; Motion/Gait tracking, recognition, and analysis; Motion segmentation and evaluation; Wearable prototypes in human-centric experiments, etc.


Keywords

The topics to be discussed in this special issue but are not limited to:
(1) Architectures and frameworks
(2) Signal processing theory and methods
(3) High speed signal processing: integrating 5G and IoT with satellite networks
(4) Agricultural signal applications
(5) Signal/image metrics
(6) Features and models for images/signals
(7) Image/signal computation and services
(8) Special signal, image and video processing applications/domains
(9) Wearable sensor signal processing

Published Papers


  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu, Shupeng Zheng
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049665
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

  • Open Access

    ARTICLE

    An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

    Yu Zuo, Wenwen Li
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049805
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the… More >

  • Open Access

    ARTICLE

    Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

    Arnab Dey, Samit Biswas, Dac-Nhuong Le
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3067-3087, 2024, DOI:10.32604/cmc.2024.049512
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers the likelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in video streams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enable instant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing action datasets often lack diversity and specificity for workout actions, hindering the development of accurate recognition models. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significant… More >

  • Open Access

    ARTICLE

    Hyperspectral Image Based Interpretable Feature Clustering Algorithm

    Yaming Kang, Peishun Ye, Yuxiu Bai, Shi Qiu
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2151-2168, 2024, DOI:10.32604/cmc.2024.049360
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral… More >

  • Open Access

    ARTICLE

    Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

    Chanho Park, Takefumi Ogawa
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.048901
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Six degrees of freedom (6DoF) input interfaces are essential for manipulating virtual objects through translation or rotation in three-dimensional (3D) space. A traditional outside-in tracking controller requires the installation of expensive hardware in advance. While inside-out tracking controllers have been proposed, they often suffer from limitations such as interaction limited to the tracking range of the sensor (e.g., a sensor on the head-mounted display (HMD)) or the need for pose value modification to function as an input interface (e.g., a sensor on the controller). This study investigates 6DoF pose estimation methods without restricting the tracking… More >

  • Open Access

    ARTICLE

    CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

    Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small… More >

  • Open Access

    REVIEW

    Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

    Shafi Ullah, Sibghat Ullah Bazai, Mohammad Imran, Qazi Mudassar Ilyas, Abid Mehmood, Muhammad Asim Saleem, Muhmmad Aasim Rafique, Arsalan Haider, Ilyas Khan, Sajid Iqbal, Yonis Gulzar, Kauser Hameed
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 93-115, 2024, DOI:10.32604/cmc.2024.048796
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the… More >

  • Open Access

    ARTICLE

    Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

    Chunbin Qin, Xiaotian Ran
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1319-1334, 2024, DOI:10.32604/cmc.2024.048850
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lacking unique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenes severely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deep learning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheral computing devices. To address these challenges, this study proposes a novel unsupervised image stitching method based on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networks and attention mechanisms. The methodology… More >

  • Open Access

    ARTICLE

    MIDNet: Deblurring Network for Material Microstructure Images

    Jiaxiang Wang, Zhengyi Li, Peng Shi, Hongying Yu, Dongbai Sun
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1187-1204, 2024, DOI:10.32604/cmc.2024.046929
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet is meticulously tailored to address the blurring in images… More >

  • Open Access

    ARTICLE

    Adaptive Segmentation for Unconstrained Iris Recognition

    Mustafa AlRifaee, Sally Almanasra, Adnan Hnaif, Ahmad Althunibat, Mohammad Abdallah, Thamer Alrawashdeh
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1591-1609, 2024, DOI:10.32604/cmc.2023.043520
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect… More >

  • Open Access

    ARTICLE

    Image Inpainting Technique Incorporating Edge Prior and Attention Mechanism

    Jinxian Bai, Yao Fan, Zhiwei Zhao, Lizhi Zheng
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 999-1025, 2024, DOI:10.32604/cmc.2023.044612
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Recently, deep learning-based image inpainting methods have made great strides in reconstructing damaged regions. However, these methods often struggle to produce satisfactory results when dealing with missing images with large holes, leading to distortions in the structure and blurring of textures. To address these problems, we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms. The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details. This… More >

  • Open Access

    ARTICLE

    Asymmetric Loss Based on Image Properties for Deep Learning-Based Image Restoration

    Linlin Zhu, Yu Han, Xiaoqi Xi, Zhicun Zhang, Mengnan Liu, Lei Li, Siyu Tan, Bin Yan
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3367-3386, 2023, DOI:10.32604/cmc.2023.045878
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Deep learning techniques have significantly improved image restoration tasks in recent years. As a crucial component of deep learning, the loss function plays a key role in network optimization and performance enhancement. However, the currently prevalent loss functions assign equal weight to each pixel point during loss calculation, which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully. To address this issue, this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task. This novel loss function… More >

  • Open Access

    ARTICLE

    Visualization for Explanation of Deep Learning-Based Fault Diagnosis Model Using Class Activation Map

    Youming Guo, Qinmu Wu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black… More >

  • Open Access

    ARTICLE

    RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

    Tian Ma, Chenhui Fu, Jiayi Yang, Jiehui Zhang, Chuyang Shang
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1103-1122, 2023, DOI:10.32604/cmc.2023.042416
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the… More >

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