Computational Imaging

This workshop will serve as a venue for presenting and discussing recent advances and trends in the growing field of computational imaging, where computation is a major component of the imaging system. Research on all aspects of the computational imaging pipeline from data acquisition (including non-traditional sensing methods) to system modeling and optimization to image reconstruction, processing, and analytics will be discussed, with talks addressing theory, algorithms and mathematical techniques, and computational hardware approaches for a variety of imaging modalities and applications such as MRI, Tomography, Ultrasound, Microscopy, Dynamic imaging, Functional imaging, Optics, Computational Photography, Radar, Lidar, Astronomical imaging, Hybrid imaging modalities, and novel and extreme imaging systems. Some talks would also explore the expanding role of computational imaging in industrial imaging applications.

Given the rapidly growing interest in data-driven, machine learning, and large-scale optimization based methods in computational imaging, the workshop will partly focus on some of the key recent and new theoretical, algorithmic, or hardware (for efficient/optimized computation) developments and challenges in these areas. Several talks will focus on analyzing, incorporating, or learning various models including sparse and low-rank models, kernel and nonlinear models, plug-and-play models, graphical, manifold, tensor, and deep convolutional or filterbank models in computational imaging problems. Research and discussion of methods and theory for new sensing techniques including data-driven sensing, task-driven imaging optimization, and online/real-time imaging optimization will be encouraged. Discussion sessions during the workshop will explore the theoretical and practical impact of various presented methods and brainstorm the main challenges and open problems.

The workshop aims to encourage close interactions between mathematical and applied computational imaging researchers and practitioners, and bring together experts in academia and industry working in computational imaging theory and applications, with focus on data and system modeling, signal processing, machine learning, inverse problems, compressed sensing, data acquisition, image analysis, optimization, neuroscience, computation-driven hardware design, and related areas, and facilitate substantive and cross-disciplinary interactions on cutting-edge computational imaging methods and systems.