Bridging Signal Processing and Generative AI: Advances in Image Modeling, Flow Matching, Diffusion Models and Deep Generative Architectures

Dear Colleagues,

Generative Artificial Intelligence (GenAI) has profoundly transformed the landscape of image and signal processing, enabling unprecedented synthesis, reconstruction, and enhancement capabilities. From denoising and super-resolution to cross-modal generation and inverse problems, GenAI frameworks have redefined how data is represented, processed, and understood.

This Special Issue aims to bring together cutting-edge research at the intersection of signal processing and generative modeling. We invite contributions exploring theoretical advances, computational models, and real-world applications that exploit the synergy between traditional signal processing principles and modern generative paradigms — including Diffusion Models, Flow Matching, GANs, VAEs, and hybrid architectures.

Flow Matching, in particular, has recently emerged as a promising successor to diffusionbased models, offering a unified framework for training deterministic generative flows via continuoustime transport equations. This paradigm bridges score-based and flow-based learning, providing more stable dynamics, efficient training, and seamless multimodal generalization.

By fostering discussion across these domains, this Special Issue seeks to consolidate the scientific foundation of Generative AI and stimulate advances that are both mathematically rigorous and practically impactful.

Topics of Interest (include but are not limited to)

• Generative Modeling for Images and Signals: Diffusion, Flow Matching, GANs, VAEs, Normalizing Flows, and hybrid architectures.
• Flow-based generative processes and transport formulations for image and signal synthesis.
• Signal Processing in Generative Pipelines: denoising, restoration, compression, superresolution, enhancement, and inverse problems.
• Cross-modal and Multimodal Systems integrating visual, auditory, textual, and sensory data.
• Physics- and Data-driven Signal Priors for stable and explainable generative reconstruction.
• Real-time and Embedded Generative Processing for edge devices and low-power platforms.
• Evaluation Metrics, Robustness, and Interpretability in Flow- and Diffusion-based generative models.
• Responsible and Ethical Use of generative signal and image technologies.
• Applications: medical imaging, remote sensing, robotics, autonomous systems, creative media, and smart infrastructure.

Dr. Clodoaldo Aparecido De Moraes Lima
Guest Editor

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