Peyman Milanfar is Professor of Electrical Engineering at the University of California, Santa Cruz. He received a B.S. degree in Electrical Engineering/Mathematics from the University of California, Berkeley, and the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology. Prior to coming to UCSC, he was at SRI (formerly Stanford Research Institute) and served as a Consulting Professor of computer science at Stanford. In 2005 he founded MotionDSP, Inc., to bring state-of-art video enhancement technology to consumer and forensic markets. He is a Fellow of the IEEE for contributions to Inverse Problems and Super-resolution in Imaging.

• Image Super-Resolution: Historical Overview and Future Challenges. Super-Resolution Using Adaptive Wiener Filters. Locally Adaptive Kernel Regression for Space-Time Super-Resolution. Super-Resolution With Probabilistic Motion Estimation. Spatially Adaptive Filtering as Regularization in Inverse Imaging. Registration for Super-Resolution. Towards Super-Resolution in the Presence of Spatially Varying Blur. Toward Robust Reconstruction-Based Super-Resolution. Multi-Frame Super-Resolution from a Bayesian Perspective. Variational Bayesian Super Resolution Reconstruction. Pattern Recognition Techniques for Image Super-Resolution. Super-Resolution Reconstruction of Multi-Channel Images. New Applications of Super-Resolution in Medical Imaging. Practicing Super-Resolution: What Have We Learned?

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Peyman Milanfar is Professor of Electrical Engineering at the University of California, Santa Cruz. He received a B.S. degree in Electrical Engineering/Mathematics from the University of California, Berkeley, and the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology. Prior to coming to UCSC, he was at SRI (formerly Stanford Research Institute) and served as a Consulting Professor of computer science at Stanford. In 2005 he founded MotionDSP, Inc., to bring state-of-art video enhancement technology to consumer and forensic markets. He is a Fellow of the IEEE for contributions to Inverse Problems and Super-resolution in Imaging.

• Image Super-Resolution: Historical Overview and Future Challenges. Super-Resolution Using Adaptive Wiener Filters. Locally Adaptive Kernel Regression for Space-Time Super-Resolution. Super-Resolution With Probabilistic Motion Estimation. Spatially Adaptive Filtering as Regularization in Inverse Imaging. Registration for Super-Resolution. Towards Super-Resolution in the Presence of Spatially Varying Blur. Toward Robust Reconstruction-Based Super-Resolution. Multi-Frame Super-Resolution from a Bayesian Perspective. Variational Bayesian Super Resolution Reconstruction. Pattern Recognition Techniques for Image Super-Resolution. Super-Resolution Reconstruction of Multi-Channel Images. New Applications of Super-Resolution in Medical Imaging. Practicing Super-Resolution: What Have We Learned?

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Peyman Milanfar is Professor of Electrical Engineering at the University of California, Santa Cruz. He received a B.S. degree in Electrical Engineering/Mathematics from the University of California, Berkeley, and the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology. Prior to coming to UCSC, he was at SRI (formerly Stanford Research Institute) and served as a Consulting Professor of computer science at Stanford. In 2005 he founded MotionDSP, Inc., to bring state-of-art video enhancement technology to consumer and forensic markets. He is a Fellow of the IEEE for contributions to Inverse Problems and Super-resolution in Imaging.

• Image Super-Resolution: Historical Overview and Future Challenges. Super-Resolution Using Adaptive Wiener Filters. Locally Adaptive Kernel Regression for Space-Time Super-Resolution. Super-Resolution With Probabilistic Motion Estimation. Spatially Adaptive Filtering as Regularization in Inverse Imaging. Registration for Super-Resolution. Towards Super-Resolution in the Presence of Spatially Varying Blur. Toward Robust Reconstruction-Based Super-Resolution. Multi-Frame Super-Resolution from a Bayesian Perspective. Variational Bayesian Super Resolution Reconstruction. Pattern Recognition Techniques for Image Super-Resolution. Super-Resolution Reconstruction of Multi-Channel Images. New Applications of Super-Resolution in Medical Imaging. Practicing Super-Resolution: What Have We Learned?

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