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Applied Sciences
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Signal and Image Processing: From Theory to Applications: 2nd Edition
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Signal and Image Processing: From Theory to Applications: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 2623

Special Issue Editors

Dr. Maria Pia Morigi

E-Mail Website
Guest Editor
Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
Interests: computed tomography; applied physics; cultural heritage
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Seracini

E-Mail Website
Guest Editor
Department of Physics and Astrophysics, University of Bologna, Viale Carlo Berti Pichat 6/2, 40127 Bologna, Italy
Interests: image processing; computed tomography; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few decades, the scientific importance of signal and image processing has experienced meaningful growth, thanks to the contributions from theoretical research and to the proliferation of new applications driven by the increased needs of both industry and consumers. Today, these new-born technologies span a wide range of scientific subjects and fields of application, ranging from medicine to industry and cultural heritage, to name only a few. Moreover, recent machine learning algorithms, based on the use of “sensed” inputs, provide an opportunity to efficiently process data and simulate behaviors that are typical of humans.

Given these premises, the opportunity arises to highlight those works involving theory and those involving applications and to combine them together.

The aim of this Special Issue is to document new applications along with their theoretical roots, especially in those topics where a sharp division between the two contributions is not possible.

The proposed Special Issue, named “Signal and Image Processing: From Theory to Applications: 2nd Edition”, includes (but is not limited to) the following topics:

  • Mathematical methods and models for signal and image processing;
  • Inverse problems in signal and image processing;
  • New applications in signal and image processing;
  • Neural networks.

Dr. Maria Pia Morigi
Dr. Marco Seracini
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.

Keywords

  • signal processing
  • image processing
  • mathematical models
  • inverse problems for signal and image processing
  • applications in signal theory
  • applications in image processing

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Related Special Issue

Published Papers (3 papers)

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Research

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31 pages, 3160 KB  
Article
Multimodal Image Segmentation with Dynamic Adaptive Window and Cross-Scale Fusion for Heterogeneous Data Environments
by Qianping He, Meng Wu, Pengchang Zhang, Lu Wang and Quanbin Shi
Appl. Sci. 2025, 15(19), 10813; https://doi.org/10.3390/app151910813 - 8 Oct 2025
Viewed by 526
Abstract
Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and [...] Read more.
Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article
(This article belongs to the Special Issue Signal and Image Processing: From Theory to Applications: 2nd Edition)
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24 pages, 4213 KB  
Article
Multi-Scale Feature Fusion and Global Context Modeling for Fine-Grained Remote Sensing Image Segmentation
by Yifan Li and Gengshen Wu
Appl. Sci. 2025, 15(10), 5542; https://doi.org/10.3390/app15105542 - 15 May 2025
Cited by 1 | Viewed by 1479
Abstract
High-precision remote sensing image semantic segmentation plays a crucial role in Earth science analysis and urban management, especially in urban remote sensing scenarios with rich details and complex structures. In such cases, the collaborative modeling of global and local contexts is a key [...] Read more.
High-precision remote sensing image semantic segmentation plays a crucial role in Earth science analysis and urban management, especially in urban remote sensing scenarios with rich details and complex structures. In such cases, the collaborative modeling of global and local contexts is a key challenge for improving segmentation accuracy. Existing methods that rely on single feature extraction architectures, such as convolutional neural networks (i.e., CNNs) and vision transformers, are prone to semantic fragmentation due to their limited feature representation capabilities. To address this issue, we propose a hybrid architecture model called PLGTransformer, which is based on dual-encoder collaborative enhancement and integrates pyramid pooling and graph convolutional network (i.e., GCN) modules. Our model innovatively constructs a parallel encoding architecture combining Swin transformer and CNN: the CNN branch captures fine-grained features such as road and building edges through multi-scale heterogeneous convolutions, while the Swin transformer branch models global dependencies of large-scale land cover using hierarchical window attention. To further strengthen multi-granularity feature fusion, we design a dual-path pyramid pooling module to perform adaptive multi-scale context aggregation for both feature types and dynamically balance local and global contributions using learnable weights. Specifically, we introduce the GCNs to build a topological graph in the feature space, enabling geometric relationship reasoning for multi-scale feature nodes at high resolution. Experiments on the Potsdam and Vaihingen datasets show that our model outperforms contemporary advanced methods and significantly improves segmentation accuracy for small objects such as vehicles and individual buildings, thereby validating the effectiveness of the multi-feature collaborative enhancement mechanism. Full article
(This article belongs to the Special Issue Signal and Image Processing: From Theory to Applications: 2nd Edition)
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Review

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30 pages, 1303 KB  
Review
Spectral Reconstruction Applied in Precision Agriculture: On-Field Solutions
by Marco Mingrone, Marco Seracini and Chiara Cevoli
Appl. Sci. 2025, 15(20), 10985; https://doi.org/10.3390/app152010985 - 13 Oct 2025
Viewed by 339
Abstract
Over the past two decades, hyperspectral imaging (HSI) systems have shown significant potential in agriculture, from disease detection to the assessment of plant and fruit nutritional status. However, most applications remain confined to laboratory analyses under controlled conditions, with only a limited fraction [...] Read more.
Over the past two decades, hyperspectral imaging (HSI) systems have shown significant potential in agriculture, from disease detection to the assessment of plant and fruit nutritional status. However, most applications remain confined to laboratory analyses under controlled conditions, with only a limited fraction implemented in field environments. In this scenario, spectral reconstruction techniques may serve as a bridge between the high accuracy of HSI and the challenges of on-field or even real-time applications. This review outlines the current state of the art of on-field HSI in the agrifood sector, highlighting existing limitations and potential advantages. It then introduces the problem of spectral reconstruction and reviews current techniques used to address it. Laboratory and on-field studies will be taken into account. The final section offers our perspective on the limitations of HSI and the promising potential of spectral super-resolution to overcome current barriers and enable broader adoption of hyperspectral technology in precision agriculture. Full article
(This article belongs to the Special Issue Signal and Image Processing: From Theory to Applications: 2nd Edition)
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