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Center for Nonlinear Studies |
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 data-adaptive. Model-based methods leverage analytical signal properties (such as wavelet sparsity) and often come with theoretical guarantees and insights. Data-adaptive 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 image recovery theory. This is achieved by specifying "denoising" as a mechanism to infuse learned priors into recovery problems, while maintaining a clear separation between the prior and physics-based acquisition models. Our methodology can fully leverage the flexibility offered by deep learning by designing learned denoisers to be used within our new family of fast iterative algorithms. Yet, our results indicate that the such algorithms can achieve state-of-the-art performance in different computational imaging tasks, while also being amenable to rigorous theoretical analysis.
Y. Sun, B. Wohlberg, and U. S. Kamilov, “An Online Plug-and-Play Algorithm for Regularized Image Reconstruction,” IEEE Trans. Comput. Imag., 2019. https://ieeexplore.ieee.org/document/8616843
Y. Sun, S. Xu, Y. Li, L. Tian, B. Wohlberg, and U. S. Kamilov, “Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2019) (Brighton, UK, May 12-17), pp. 7665–7669.https://ieeexplore.ieee.org/document/8683057
Y. Sun, J. Liu, and U. S. Kamilov, “Block Coordinate Regularization by Denoising.” https://arxiv.org/abs/1905.05113
Host: Brendt Wohlberg