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Electronics
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Image and Signal Processing Techniques and Applications
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Image and Signal Processing Techniques and Applications

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

Deadline for manuscript submissions: 15 April 2026 | Viewed by 3733

Special Issue Editors

Dr. Hanlin Mo

E-Mail Website
Guest Editor
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, 90570 Oulu, Finland
Interests: convolutional neural network; image processing; action recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Shuang Wang

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an 710071, China
Interests: image sparse representation and compression sensing theory; image denoising and segmentation; SAR image denoising and terrain classification; SAR target detection and recogn
Dr. Yu Gu

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: image registration; domain adaptation; multimodal learning; intelligent signal processing

Special Issue Information

Dear Colleagues,

Image and signal processing techniques have made remarkable progress over the past two decades and have been applied in various scenarios. The core step in image and signal analysis is feature extraction, which can be categorized into two primary approaches: knowledge-driven handcrafted features and data-driven deep learning methods. The former excels in feature interpretability, computational efficiency, and performance predictability, while the latter demonstrates significant advantages in feature generalization, high-level semantic representation, and multi-modal/heterogeneous data modeling and has achieved remarkable results on large-scale datasets supporting various image and signal analysis tasks. Recently, researchers have started exploring knowledge-data collaborative approaches for feature extraction in image and signal processing, focusing on algorithm reliability, feature interpretability, and model parameter lightweighting. Moreover, the emergence of large language models and vision–language models has further advanced research in multi-modal heterogeneous data feature alignment and fusion for image and signal processing. This Special Issue invites authors to submit their latest research on image and signal processing methods, particularly those related to feature extraction (including both handcrafted and deep learning-based methods). Possible contributions include, but are not limited to, robust/invariant feature extraction, feature interpretability, lightweight deep learning models, multi-modal feature alignment and fusion, as well as other related research in the fields of computer vision, pattern recognition, and signal processing. Additionally, application-oriented studies in areas such as remote sensing, affective computing, and industrial production are highly welcome.

Dr. Hanlin Mo
Dr. Shuang Wang
Dr. Yu Gu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • computer vision
  • signal processing
  • feature extraction
  • handcrafted features
  • deep neural networks
  • feature reliability and interpretability
  • lightweight deep learning
  • multi-modal feature alignment and fusion

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Published Papers (5 papers)

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Research

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23 pages, 4423 KB  
Article
Softmax-Derived Brain Age Mapping: An Interpretable Visualization Framework for MRI-Based Brain Age Prediction
by Ting-An Chang, Shao-Yu Yan, Kuan-Chih Wang and Chung-Wen Hung
Electronics 2026, 15(1), 220; https://doi.org/10.3390/electronics15010220 - 2 Jan 2026
Viewed by 386
Abstract
Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, [...] Read more.
Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, we developed a VGGNet-based brain age prediction model and proposed the Softmax-Derived Brain Age Mapping algorithm to simultaneously identify brain regions associated with both youthful and aging features. The resulting saliency maps provide explicit representations of developmental and degenerative processes across different brain regions. Brain Age Map analysis revealed that aging features in the healthy group were primarily confined to the frontal cortex, aligning with findings that the frontal lobe is the earliest region to undergo natural senescence. In contrast, the dementia group exhibited widespread aging across the frontal, temporal, parietal, and occipital lobes, as well as the ventricular regions. These results suggest that the spatial distribution of brain aging can serve as a critical biomarker for distinguishing normal aging trajectories from pathological degeneration. From an application perspective, we further explored the potential of the proposed framework in neurodegenerative diseases. The analysis reveals that dementia patients generally exhibit an advanced brain age, with cortical aging being markedly more pronounced than in age-matched healthy samples. Notably, although dementia cases were not included in the training set, the model was still able to localize abnormalities in relevant brain regions, underscoring its potential value as an assistive tool for early dementia diagnosis. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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26 pages, 6034 KB  
Article
BiLSTM-FuseNet: A Deep Fusion Model for Denoising High-Noise Near-Infrared Spectra
by Jianxia Xue, Xiaojing Chen and Soo-Hyung Kim
Electronics 2026, 15(1), 206; https://doi.org/10.3390/electronics15010206 - 1 Jan 2026
Viewed by 232
Abstract
Near-infrared spectroscopy (NIRS) is widely used in food, pharmaceutical, and agricultural analyses but is highly susceptible to noise. To address this, we propose BiLSTM-FuseNet, a denoising framework that combines temporal modeling and explicit noise estimation. It uses stacked Bidirectional Long Short-Term Memory (BiLSTM) [...] Read more.
Near-infrared spectroscopy (NIRS) is widely used in food, pharmaceutical, and agricultural analyses but is highly susceptible to noise. To address this, we propose BiLSTM-FuseNet, a denoising framework that combines temporal modeling and explicit noise estimation. It uses stacked Bidirectional Long Short-Term Memory (BiLSTM) layers for global–local spectral learning and an MLP branch to predict and subtract noise. Evaluated on the Tablet and AnHui soil datasets with various synthetic noise types, the model outperformed the conventional methods, achieving an RMSE of 0.024 and R2 of 0.68 under mixed noise. The downstream regression improved the tablet weight prediction R2 from 0.079 to 0.218. These findings demonstrate the robustness of BiLSTM-FuseNet and its clear advantages for practical downstream NIR applications. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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27 pages, 8913 KB  
Article
Laser Radar and Micro-Light Polarization Image Matching and Fusion Research
by Jianling Yin, Gang Li, Bing Zhou and Leilei Cheng
Electronics 2025, 14(15), 3136; https://doi.org/10.3390/electronics14153136 - 6 Aug 2025
Viewed by 984
Abstract
Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering [...] Read more.
Aiming at addressing the defect of the data blindness of a LiDAR point cloud in transparent media such as glass in low illumination environments, a new method is proposed to realize covert target reconnaissance, identification and ranging using the fusion of a shimmering polarized image and a laser LiDAR point cloud, and the corresponding system is constructed. Based on the extraction of pixel coordinates from the 3D LiDAR point cloud, the method adds information on the polarization degree and polarization angle of the micro-light polarization image, as well as on the reflective intensity of each point of the LiDAR. The mapping matrix of the radar point cloud to the pixel coordinates is made to contain depth offset information and show better fitting, thus optimizing the 3D point cloud converted from the micro-light polarization image. On this basis, algorithms such as 3D point cloud fusion and pseudo-color mapping are used to further optimize the matching and fusion procedures for the micro-light polarization image and the radar point cloud, so as to successfully realize the alignment and fusion of the 2D micro-light polarization image and the 3D LiDAR point cloud. The experimental results show that the alignment rate between the 2D micro-light polarization image and the 3D LiDAR point cloud reaches 74.82%, which can effectively detect the target hidden behind the glass under the low illumination condition and fill the blind area of the LiDAR point cloud data acquisition. This study verifies the feasibility and advantages of “polarization + LiDAR” fusion in low-light glass scene reconnaissance, and it provides a new technological means of covert target detection in complex environments. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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24 pages, 5346 KB  
Article
Scene-Speaker Emotion Aware Network: Dual Network Strategy for Conversational Emotion Recognition
by Bingni Li, Yu Gu, Chenyu Li, He Zhang, Linsong Liu, Haixiang Lin and Shuang Wang
Electronics 2025, 14(13), 2660; https://doi.org/10.3390/electronics14132660 - 30 Jun 2025
Viewed by 1110
Abstract
Incorporating external knowledge has been shown to improve emotion understanding in dialogues by enriching contextual information, such as character motivations, psychological states, and causal relations between events. Filtering and categorizing this information can significantly enhance model performance. In this paper, we present an [...] Read more.
Incorporating external knowledge has been shown to improve emotion understanding in dialogues by enriching contextual information, such as character motivations, psychological states, and causal relations between events. Filtering and categorizing this information can significantly enhance model performance. In this paper, we present an innovative Emotion Recognition in Conversation (ERC) framework, called the Scene-Speaker Emotion Awareness Network (SSEAN), which employs a dual-strategy modeling approach. SSEAN uniquely incorporates external commonsense knowledge describing speaker states into multimodal inputs. Using parallel recurrent networks to separately capture scene-level and speaker-level emotions, the model effectively reduces the accumulation of redundant information within the speaker’s emotional space. Additionally, we introduce an attention-based dynamic screening module to enhance the quality of integrated external commonsense knowledge through three levels: (1) speaker-listener-aware input structuring, (2) role-based segmentation, and (3) context-guided attention refinement. Experiments show that SSEAN outperforms existing state-of-the-art models on two well-adopted benchmark datasets in both single-text modality and multimodal settings. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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Review

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40 pages, 47306 KB  
Review
Advances in EMG Signal Processing and Pattern Recognition: Techniques, Challenges, and Emerging Applications
by Lasitha Piyathilaka, Jung-Hoon Sul, Sanura Dunu Arachchige, Amal Jayawardena and Diluka Moratuwage
Electronics 2026, 15(3), 590; https://doi.org/10.3390/electronics15030590 - 29 Jan 2026
Viewed by 340
Abstract
Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing [...] Read more.
Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems. Full article
(This article belongs to the Special Issue Image and Signal Processing Techniques and Applications)
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