Novel Deep Learning Architectures, Algorithms, and Systems for Signal and Image Processing
A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".
Deadline for manuscript submissions: 25 November 2026 | Viewed by 65
Special Issue Editor
Interests: deep learning; machine learning; reinfrocement learning; EEG; biomedical signals; signal processing; Riemannian geometry; brain signals; ECG; EMG; chaos; fractal geometry; algorithms; bio-inspired; meta heuristics; XAI; diffusion models; hyperspectral; RE
Special Issue Information
Dear Colleagues,
Recent progress in deep learning has transformed signal and image processing, not only by improving analytical performance but also by enabling the invention of new computational methods, intelligent processing frameworks, and integrated application-oriented systems. Beyond conventional performance gains, current research is increasingly focused on the design of novel model architectures, hybrid processing pipelines, hardware-aware implementations, explainable decision mechanisms, and deployable solutions that address concrete technical and industrial challenges.
This Special Issue aims to collect original research articles and comprehensive reviews on inventive deep learning approaches for signal and image processing. In particular, it welcomes contributions that introduce new models, algorithmic strategies, system-level designs, or interdisciplinary processing frameworks with clear novelty and practical relevance. Topics of interest include, but are not limited to, innovative deep architectures for classification, segmentation, detection, denoising, restoration, reconstruction, prediction, fusion, and compression of signal and image data; transformer-based, convolutional, graph-based, and hybrid neural models; self-supervised, weakly supervised, multimodal, and generative learning strategies; explainable and trustworthy artificial intelligence; lightweight and energy-efficient networks; edge and embedded implementations; domain adaptation; transfer learning; and robust learning under limited, noisy, or imbalanced data conditions.
Special consideration will be given to contributions that demonstrate invention-oriented advances through new algorithmic formulations, novel processing paradigms, integrated intelligent devices or platforms, and technically sound validation in real-world scenarios. Application domains may include biomedical and healthcare systems, medical imaging, remote sensing, industrial inspection, autonomous systems, multimedia technologies, surveillance, smart manufacturing, and human–machine interaction. By focusing on inventive and practically relevant developments, this Special Issue seeks to provide a platform for emerging ideas and enabling technologies that advance the next generation of deep learning-driven signal and image processing.
Dr. Imad Eddine Tibermacine
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- inventive methods
- signal processing
- image processing
- intelligent systems
- hybrid architectures
- multimodal learning
- generative models
- explainable artificial intelligence
- edge intelligence
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