Aims and scope

The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles.

Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications.

The scope of the journal includes:

computational models of vision; imaging algebra and mathematical morphology
mathematical methods in reconstruction, compactification, and coding
filter theory
probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science
inverse optics
wave theory.

Specific application areas of interest include, but are not limited to:

all aspects of image formation and representation
medical, biological, industrial, geophysical, astronomical and military imaging
image analysis and image understanding
parallel and distributed computing
computer vision architecture design.
The journal emphasizes its commitment to present coverage in these areas which span the interest of a global audience. The Editorial Board publishes manuscripts that are well-written and make a significant technical contrib ution.

Special Issue on Variational Image Regularisation in the Era of Deep Learning: From Model-Based to Deep Priors and Back
Día de Entrega: 2024-11-20

The Journal of Mathematical Imaging and Vision is pleased to announce a forthcoming special issue dedicated to the theme of "Variational Image regularization in the Era of Deep Learning: From Model-Based to Deep Priors and Back." This special issue aims to explore the impact of deep learning on variational image regularization techniques, bridging traditional model-based methods with innovative deep learning approaches.

In the last decade, the field of image processing has been subject to a paradigm shift due to the advent of deep learning. Traditional variational methods, which rely on handcrafted priors and energy minimization techniques, have been complemented and, in some cases, supplanted by deep learning models capable of learning complex image priors from data. However, the integration of these approaches offers a promising avenue for leveraging the strengths of both paradigms.

This special issue will cover a broad spectrum of topics, including but not limited to:

- Advances in model-based variational methods and their modern applications.
- The role of deep learning in enhancing variational image regularization and vice versa.
- Hybrid approaches combining variational models and deep neural networks.
- Theoretical insights into the convergence and stability of deep priors in variational frameworks.
- Practical applications of combined variational and deep learning methods in computational imaging with applications including medical imaging, remote sensing, among others

We invite researchers to contribute original research articles, comprehensive reviews, and insightful perspectives that shed light on the synergy between variational methods and deep learning. This special issue aims to foster a deeper understanding of how these methodologies can be effectively combined, pushing the boundaries of what is possible in image regularisation and analysis.

By bringing together contributions from leading experts, this special issue seeks to serve as a cornerstone for future research at the intersection of variational methods and deep learning, providing a robust framework for ongoing advancements in the field.