The mathematical study of image reconstruction problems can have a huge impact on human life.
More efficient mathematical algorithms for X-ray tomography and more accurate mathematical
models in seismic or hybrid imaging can lead to better imaging devices in fields such as medicine
and remote sensing. Developing the underlying mathematics, including the analysis of reconstruction
stability, regularization, singularity characterization, and efficient algorithms, may lead to
fewer false positives in fields such as medical, seismic and radar imaging.
This topical workshop will bring together international experts working in computational
and analytical aspects of image reconstruction (including but not limited to electron-microscope
tomography, hybrid imaging, radar and sonar, full waveform inversion of seismic imaging and X-ray CT)
as well as postdoctoral fellows and graduate students. There will be multiple introductory-level talks
for early-career researchers and non-specialists in the area on both the mathematics involved and the
scientific and industrial applications. Speakers and participants from industry will be included to
strengthen the practical aspects of the workshop.
Output of iterations of a Mumford-Shah level set-based method
for simultaneous reconstruction and segmentation of a torso phantom
from noisy CT data. Images courtesy of Esther Klann relating to work
in E. Klann, R. Ramlau, and W. Ring, Inverse Problems and Imaging,
Vol. 5 (2011), 137-166.