“Computational Imaging: Reconciling Model-Based and Learning-Based Algorithms”, Ulugbek S. Kamilov, Washington University in St. Louis, Tuesday, Nov 19, 11am, Bldg 401, Room A1100
Abstract: There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquisition. There are currently two distinct viewpoints on addressing such information loss: model-based and learning-based. Model-based methods leverage analytical signal properties (such as wavelet sparsity) and often come with theoretical guarantees and insights. Learning-based methods leverage flexible representations (such as convolutional neural nets) for best empirical performance through training on big datasets. The goal of this talk is to introduce a framework that reconciles both viewpoints by providing the “deep learning” counterpart of the classical optimization theory. This is achieved by specifying “denoising deep neural nets” as a mechanism to infuse learned priors into recovery problems, while maintaining a clear separation between the prior and physics-based acquisition models.