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Mathematics in Imaging
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Mathematical Imaging covers mathematical and computational methods for modeling the physics of imaging methods and for providing stable and resolved solutions to the inverse problem of image reconstruction.
It captures leading edge fundamental developments in imaging science and has applications in photonics, biomedical imaging, astronomy, and remote sensing. Imaging methods are constrained by the experimental data which are corrupted by measurement and medium noises and depend nonlinearly on the imaged object material and geometric parameters. Many of the fundamental problems in image reconstruction methods are shared across different disciplines, including optics, acoustics, seismic imaging, and synthetic aperture radar. These include optimal representations, resolution and stability estimates, optimal design approaches, and model reduction techniques.
Mathematical Imaging provides a platform for a cross fertilization between mathematical, computational, physical, and engineering disciplines related to imaging and inverse problems. Because of the breadth involved, papers highlighting either the interdisciplinary flavor of imaging methodologies or the fundamental mathematical and computational issues in imaging are particularly encouraged.
- Inverse scattering, regularization, constraints, inversion in the multiple scattering regime
- Shape optimization
- Blind deconvolution
- Coherent imaging systems (holographic imaging, synthetic aperture radar ...)
- Phaseless imaging (X-ray imaging ...)
- 3D imaging (tomographies, microscopy ...)
- Imaging through turbulent, random or highly scattering media
- Biomedical imaging and hybrid imaging
- Biomimetic imaging
- Inverse problems related to design of metamaterials, metasurfaces, and cloaking
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