Machine Learning and Compressed Sensing in Image Reconstruction

Dear Colleagues,

The development of fast and accurate reconstruction algorithms plays a central role in modern imaging systems. Examples include x-ray tomography, ultrasound imaging, photoacoustic imaging, super-resolution imaging, and magnetic resonance imaging. Compressed sensing and machine learning are successful tools for various imaging applications. Compressed sensing techniques allow to significantly reduce the amount of data to be acquired and thereby accelerates data acquisition, reduces motion artefacts, and lowers radiation exposure. In compressed sensing, iterative algorithms based on prior information have been applied for image reconstruction. Such algorithms can be time-consuming as the forward and adjoint problems have to be computed repeatedly. Recently, a new class of algorithms based on machine learning, especially deep learning, for compressed sensing and other image reconstruction tasks appeared. With deep learning, image reconstruction can be performed efficiently using artificial neural networks, whose weights are based on training data. While still in their infancy, these techniques already show astonishing performance. This Special Issue focuses on the latest research and development of compressed sensing and machine learning for image reconstruction. Papers on the design of new reconstruction algorithms and new compressed sensing and machine learning applications are welcome. In addition, contributions on the theoretical analysis and understanding of compressed sensing and machine learning in image reconstruction are welcome.

Prof. Dr. Markus Haltmeier
Prof. Dr. Linh. V. Nguyen
Guest Editors

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