G
Guoshen Yu
Researcher at University of Minnesota
Publications - 12
Citations - 1339
Guoshen Yu is an academic researcher from University of Minnesota. The author has contributed to research in topics: Mixture model & Compressed sensing. The author has an hindex of 8, co-authored 12 publications receiving 1257 citations.
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Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
TL;DR: In this article, a general framework for image inverse problems is introduced, based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared to traditional sparse inverse problem techniques.
Journal ArticleDOI
Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
TL;DR: A dual mathematical interpretation of the proposed framework with a structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques.
Proceedings ArticleDOI
Image modeling and enhancement via structured sparse model selection
TL;DR: An image representation framework based on structured sparse model selection is introduced, leading to a guaranteed near optimal denoising estimator and state-of-the-art results are shown in image Denoising, deblurring, and inpainting.
Journal ArticleDOI
Statistical Compressed Sensing of Gaussian Mixture Models
Guoshen Yu,Guillermo Sapiro +1 more
TL;DR: In real image sensing applications, GMM-based SCS is shown to lead to improved results compared to conventional CS, at a considerably lower computational cost.
Journal ArticleDOI
Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models
TL;DR: A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classicalcompressive sensing and optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction are proposed.