OPTI556A

043081

Computational imaging consists of joint design of measurement strategy and estimation algorithms. Tomography, consisting of estimation of multidimensional objects from lower dimensional measurements, is the core challenge. This course reviews principles of forward model and inversion algorithms for computational imaging and analyzes imaging systems for geometric and coherent wave field models. Forward models, consisting of discrete representations of continuous image and measurement spaces, are fundamental to computational imaging. The course reviews how to form and evaluate such models. Image estimation combines linear regression and artificial neural networks. Convolutional networks, neural representations and transformer networks are reviewed. Coded aperture and structured illumination systems are considered for x-ray imaging, phase retrieval, holography and wave front sensing are considered for wave imaging.

GRD - Regular Grades A, B, C, D, E

Graduate

Graduate standing in optical sciences.