Stanley Chan, Jeff Fessler, Justin Haldar, Ulugbek Kamilov, Saiprasad Ravishankar, Rebecca Willett, Brendt Wohlberg just organized a workshop at IMA on computational imaging. Short story as this blog just passed the 8 million page views. Understanding of Compressed sensing was in large part, at least by looking at the stats:hits on this blog, due to an IMA meeting on the subject and the fact that people could watch the videos afterward. Hoping for this workshop to follow the same path. Given the amount of ML in it, I wonder if it shouldn't have been called TheGreatConvergence meeting:-)
This workshop will serve as a venue for presenting and discussing recent advances and trends in the growing field of computational imaging, where computation is a major component of the imaging system. Research on all aspects of the computational imaging pipeline from data acquisition (including non-traditional sensing methods) to system modeling and optimization to image reconstruction, processing, and analytics will be discussed, with talks addressing theory, algorithms and mathematical techniques, and computational hardware approaches for imaging problems and applications including MRI, tomography, ultrasound, microscopy, optics, computational photography, radar, lidar, astronomical imaging, hybrid imaging modalities, and novel and extreme imaging systems. The expanding role of computational imaging in industrial imaging applications will also be explored.
Given the rapidly growing interest in data-driven, machine learning, and large-scale optimization based methods in computational imaging, the workshop will partly focus on some of the key recent and new theoretical, algorithmic, or hardware (for efficient/optimized computation) developments and challenges in these areas. Several talks will focus on analyzing, incorporating, or learning various models including sparse and low-rank models, kernel and nonlinear models, plug-and-play models, graphical, manifold, tensor, and deep convolutional or filterbank models in computational imaging problems. Research and discussion of methods and theory for new sensing techniques including data-driven sensing, task-driven imaging optimization, and online/real-time imaging optimization will be encouraged. Discussion sessions during the workshop will explore the theoretical and practical impact of various presented methods and brainstorm the main challenges and open problems.
The workshop aims to encourage close interactions between mathematical and applied computational imaging researchers and practitioners, and bring together experts in academia and industry working in computational imaging theory and applications, with focus on data and system modeling, signal processing, machine learning, inverse problems, compressed sensing, data acquisition, image analysis, optimization, neuroscience, computation-driven hardware design, and related areas, and facilitate substantive and cross-disciplinary interactions on cutting-edge computational imaging methods and systems.
- Morning Theme: Optics/Photography
- Welcome to the IMA Saiprasad Ravishankar (Michigan State University), Brendt Wohlberg (Los Alamos National Laboratory)
- Computational Microscopy Laura Waller (University of California, Berkeley)
- Interpreting Photon and Electron Detections to Form Images Vivek Goyal (Boston University)
- Q&A / Coffee
- Tutorial on Julia Programming for Computational Imaging Jeff Fessler (University of Michigan)
- Surfing the Technology Wave 2019 and Algorithm-Hardware Co-Design - Industry Perspective + Q&A Sergio Goma (QUALCOMM)
- Afternoon Theme: Optimization for Computational Imaging (CI)
- Dictionary and model-based methods in quantitative MRI reconstruction Mariya Doneva (Philips Research Laboratory)
- A simple 2D graphing tool for the convergence of fixed-point iterations and plug-and-play methods Wotao Yin (University of California, Los Angeles)
- Data Compression in Distributed Learning Ming Yan (Michigan State University)
- Morning Theme: Novel Imaging Domains Rebecca Willett (University of Chicago)
- Cryo-Electron Microscopy Image Analysis with Multi-Frequency Vector Diffusion Maps Zhizhen (Jane) Zhao (University of Illinois at Urbana-Champaign)
- Imaging the Unseen: Taking the First Picture of a Black Hole Katie Bouman (California Institute of Technology)
- Q&A
- Computational Methods for Large-scale Inverse Problems: Data-driven VS Physics-driven or Combined? Youzuo Lin (Los Alamos National Laboratory)
- Affiliated Data Science Seminar: Simple Approaches to Complicated Data Analysis Deanna Needell (University of California, Los Angeles)
- Afternoon Theme: Perspectives on Machine Learning and Artificial Intelligence for CI
- Tutorial on Python Programming for Computational Imaging Frank Ong (Stanford University)
- Tutorial Part II
- Computational Imaging with Deep Learning Orazio Gallo (NVIDIA Corporation)
- Poster Session and Reception
- Morning Theme: Perspectives on Machine Learning and Artificial Intelligence for CI
- Jeff Fessler (University of Michigan)
- Coherent Optical Processing with Machine Learning Charles Bouman (Purdue University)
- Faster Guaranteed GAN-based recovery in Linear Inverse Problems Yoram Bresler (University of Illinois at Urbana-Champaign)
- Q&A
- Geometry of Convolutional Neural Networks for Computational Imaging Jong Chul Ye (Korea Advanced Institute of Science and Technology (KAIST))
- Afternoon Theme: X-Ray Computed Tomography Imaging
- Novel CT Data Acquisition and Processing Joseph Stayman (Johns Hopkins University)
- Machine Learning for Tomographic Imaging Ge Wang (Rensselaer Polytechnic Institute)
- Q&A/Coffee Break
- Data and Image Domain Deep Learning for Tomographic Computational Imaging W. Clem Karl (Boston University)
- Q&A
- IEEE Computational Imaging Technical Committee Meeting (open to all)
- Morning Theme: Signal Processing for CI Stanley Chan (Purdue University), Justin Haldar (University of Southern California)
- Three Short Stories About Image Denoising Mario Figueiredo (Instituto Superior Tecnico)
- Fourier Multispectral Imaging Keigo Hirakawa (University of Dayton)
- Q&A
- Modeling and removal of correlated noise using nonlocal patch-based collaborative filters, with applications to direct and inverse imaging Alessandro Foi (Tampere University of Technology)
- Afternoon Theme: Magnetic Resonance Imaging
- Computational Imaging: Beyond the limits imposed by lenses Ashok Veeraraghavan (Rice University)
- Q&A / Coffee
- Computational Imaging: From structured low-rank methods to model based deep learning. Mathews Jacob (The University of Iowa)
- Q&A/Coffee Break
- Rise of the machines (in MR image reconstruction) Florian Knoll (NYU Langone Medical Center)
- Q&A
- Theme: Broad CI Modalities Ulugbek Kamilov (Washington University)
- Computational Radar Imaging Mujdat Cetin (University of Rochester)
- Q&A
- Challenges and Opportunities in Magnetic Resonance Fingerprinting Nicole Seiberlich (University of Michigan)
- Q&A/Closing Remarks Brendt Wohlberg (Los Alamos National Laboratory)
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Great stuff, thanks for posting this, Igor!
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