Dates currently being confirmed; check back.

Call for Papers Announced 2 May
Journal-first (JIST/JPI) Submissions

∙ Submission site Opens 2 May 
∙ Journal-first (JIST/JPI) Submissions Due 1 Aug
∙ Final Journal-first manuscripts due 28 Oct
Conference Papers Submissions
∙ Abstract Submission Opens 1 June
∙ Priority Decision Submission Ends 15 July
∙ Extended Submission Ends  19 Sept
∙ FastTrack Conference Proceedings Manuscripts Due 25 Dec 
∙ All Outstanding Proceedings Manuscripts Due
 6 Feb 2023
Registration Opens 1 Dec
Demonstration Applications Due 19 Dec
Early Registration Ends 18 Dec

Hotel Reservation Deadline 6 Jan
Symposium begins
15 Jan

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High Performance Computing for Imaging 2023

Monday 16 January 2023

AI Methods for Imaging (M1)

Session Chair: Muralikrishnan Gopalakrishnan Meena, Oak Ridge National Laboratory (United States)
9:05 – 10:20 AM

Conference Welcome

HPC+AI-enabled real-time nanoscale x-ray imaging (Invited), Mathew Cherukara, Argonne National Laboratory (United States) [view abstract]


Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications, Muralikrishnan Gopalakrishnan Meena1, Amir K. Ziabari1, Singanallur Venkatakrishnan1, Isaac R. Lyngaas1, Matthew R. Norman1, Balint Joo1, Thomas L. Beck1, Charles A. Bouman2, Anuj Kapadia1, and Xiao Wang1; 1Oak Ridge National Laboratory and 2Purdue University (United States) [view abstract]


WearMask: Fast in-browser face mask detection with serverless edge computing for COVID-19, Zekun Wang1, Pengwei Wang2, Peter C. Louis3, Lee E. Wheless3, and Yuankai Huo1; 1Vanderbilt University (United States), 2Shandong University (China), and 3Vanderbilt University Medical Center (United States) [view abstract]


10:20 – 10:50 AM Coffee Break

Ptychographic Imaging (M2)

Session Chair: Xiao Wang, Oak Ridge National Laboratory (United States)
10:50 AM – 12:10 PM

High-performance ptychographic reconstruction using GPUs (Invited), Tekin Bicer, Argonne National Laboratory (United States) [view abstract]


Image gradient decomposition for parallel and memory-efficient ptychographic reconstruction (Invited), Xiao Wang, Oak Ridge National Laboratory (United States) [view abstract]


AI-assisted automated workflow for real-time x-ray ptychography data analysis via federated resources, Anakha V Babu, Tekin Bicer, Saugat Kandel, Tao Zhou, Daniel J. Ching, Steven Henke, Sinisa Veseli, Ryan Chard, Antonio Miceli, and Mathew Cherukara, Argonne National Laboratory (United States) [view abstract]


12:30 – 2:00 PM Lunch

Monday 16 January PLENARY: Neural Operators for Solving PDEs

Session Chair: Robin Jenkin, NVIDIA Corporation (United States)
2:00 PM – 3:00 PM
Cyril Magnin I/II/III

Deep learning surrogate models have shown promise in modeling complex physical phenomena such as fluid flows, molecular dynamics, and material properties. However, standard neural networks assume finite-dimensional inputs and outputs, and hence, cannot withstand a change in resolution or discretization between training and testing. We introduce Fourier neural operators that can learn operators, which are mappings between infinite dimensional spaces. They are independent of the resolution or grid of training data and allow for zero-shot generalization to higher resolution evaluations. When applied to weather forecasting, neural operators capture fine-scale phenomena and have similar skill as gold-standard numerical weather models for predictions up to a week or longer, while being 4-5 orders of magnitude faster.

Anima Anandkumar, Bren professor, California Institute of Technology, and senior director of AI Research, NVIDIA Corporation (United States)


Anima Anandkumar is a Bren Professor at Caltech and Senior Director of AI Research at NVIDIA. She is passionate about designing principled AI algorithms and applying them to interdisciplinary domains. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. Anandkumar received her BTech from Indian Institute of Technology Madras, her PhD from Cornell University, and did her postdoctoral research at MIT and assistant professorship at University of California Irvine.

3:00 – 3:30 PM Coffee Break

EI 2023 Highlights Session

Session Chair: Robin Jenkin, NVIDIA Corporation (United States)
3:30 – 5:00 PM
Cyril Magnin II

Join us for a session that celebrates the breadth of what EI has to offer with short papers selected from EI conferences.

NOTE: The EI-wide "EI 2023 Highlights" session is concurrent with Monday afternoon COIMG, COLOR, IMAGE, and IQSP conference sessions.


Evaluation of image quality metrics designed for DRI tasks with automotive cameras, Valentine Klein, Yiqi LI, Claudio Greco, Laurent Chanas, and Frédéric Guichard, DXOMARK (France) [view abstract]


Human performance using stereo 3D in a helmet mounted display and association with individual stereo acuity, Bonnie Posselt, RAF Centre of Aviation Medicine (United Kingdom) [view abstract]


Smartphone-enabled point-of-care blood hemoglobin testing with color accuracy-assisted spectral learning, Sang Mok Park1, Yuhyun Ji1, Semin Kwon1, Andrew R. O’Brien2, Ying Wang2, and Young L. Kim1; 1Purdue University and 2Indiana University School of Medicine (United States) [view abstract]


Designing scenes to quantify the performance of automotive perception systems, Zhenyi Liu1, Devesh Shah2, Alireza Rahimpour2, Joyce Farrell1, and Brian Wandell1; 1Stanford University and 2Ford Motor Company (United States) [view abstract]


Visualizing and monitoring the process of injection molding, Christian A. Steinparz1, Thomas Mitterlehner2, Bernhard Praher2, Klaus Straka1,2, Holger Stitz1,3, and Marc Streit1,3; 1Johannes Kepler University, 2Moldsonics GmbH, and 3datavisyn GmbH (Austria) [view abstract]


Commissioning the James Webb Space Telescope, Joseph M. Howard, NASA Goddard Space Flight Center (United States) [view abstract]


Critical flicker frequency (CFF) at high luminance levels, Alexandre Chapiro1, Nathan Matsuda1, Maliha Ashraf2, and Rafal Mantiuk3; 1Meta (United States), 2University of Liverpool (United Kingdom), and 3University of Cambridge (United Kingdom) [view abstract]


Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications, Muralikrishnan Gopalakrishnan Meena1, Amir K. Ziabari1, Singanallur Venkatakrishnan1, Isaac R. Lyngaas1, Matthew R. Norman1, Balint Joo1, Thomas L. Beck1, Charles A. Bouman2, Anuj Kapadia1, and Xiao Wang1; 1Oak Ridge National Laboratory and 2Purdue University (United States) [view abstract]


Layered view synthesis for general images, Loïc Dehan, Wiebe Van Ranst, and Patrick Vandewalle, Katholieke University Leuven (Belgium) [view abstract]


A self-powered asynchronous image sensor with independent in-pixel harvesting and sensing operations, Ruben Gomez-Merchan, Juan Antonio Leñero-Bardallo, and Ángel Rodríguez-Vázquez, University of Seville (Spain) [view abstract]


Color blindness and modern board games, Alessandro Rizzi1 and Matteo Sassi2; 1Università degli Studi di Milano and 2consultant (Italy) [view abstract]


5:00 – 6:15 PM EI 2023 All-Conference Welcome Reception (in the Cyril Magnin Foyer)

Tuesday 17 January 2023

KEYNOTE: High-Performance Imaging (T1)

Session Chair: Xiao Wang, Oak Ridge National Laboratory (United States)
8:50 – 10:20 AM

KEYNOTE: Reducing the barriers to high performance imaging, Charles A. Bouman, Purdue University (United States) [view abstract]

Prof. Charles A. Bouman received a BSEE from the University of Pennsylvania in 1981 and an MS from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a PhD in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering. Prof. Bouman’s research is in the area of computational imaging and sensing where he is focused on the integration of signal processing, statistical modeling, physics, and computation to solve difficult sensing problems with applications in healthcare, material science, physics, chemistry, commercial and consumer imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products. Professor Bouman is a member of the National Academy of Inventors, a Fellow of the IEEE, a Fellow and Honorary Member of the society for Imaging Science and Technology (IS&T), a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), and a Fellow of the SPIE professional society. He is the recipient of the 2021 IEEE Signal Processing Society, Claude Shannon-Harry Nyquist Technical Achievement Award, the 2014 Electronic Imaging Scientist of the Year award, and the IS&T’s Raymond C. Bowman Award; and in 2020, his paper on Plug-and-Play Priors won the SIAM Imaging Science Best Paper Prize. He was previously the Editor-in-Chief for the IEEE Transactions on Image Processing; a Distinguished Lecturer for the IEEE Signal Processing Society; and a Vice President of Technical Activities for the IEEE Signal Processing Society, during which time he led the creation of the IEEE Transactions on Computational Imaging. He has been an associate editor for the IEEE Transactions on Image Processing, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the SIAM Journal on Mathematical Imaging. He has also been a Vice President of Publications and a member of the Board of Directors for the IS&T Society, and he is the founder and Co-Chair of the long running IS&T conference on Computational Imaging.


High-performance embedded imaging: An optics, sensor, and computing co-design approach (Invited), Yuhao Zhu, University of Rochester (United States) [view abstract]


10:00 AM – 7:30 PM Industry Exhibition - Tuesday (in the Cyril Magnin Foyer)

10:20 – 10:50 AM Coffee Break

X-ray Scatter and MR Imaging (T2)

Session Chair: Venkatesh Sridhar, Lawrence Livermore National Laboratory (United States)
10:50 AM – 12:10 PM

A system for large-scale inverse multiple-scattering imaging on GPU supercomputers with real data (Invited), Mert Hidayetoglu; Stanford University and SLAC National Accelerator Laboratory (United States) [view abstract]


Fast massively parallel physics-based algorithm for modeling multi-order scatter in CT (Invited), Venkatesh Sridhar1, Xin Liu1, and Kyle Champley2; 1Lawrence Livermore National Laboratory and 2Ziteo Medical (United States) [view abstract]


Clinical validation of rapid GPU-enabled DTI tractography of the brain, Felix Liu1, Vanitha Sankaranarayanan1, Javier Villanueva-Meyer1, Shawn Hervey-Jumper1, James Hawkins1, Pablo Damasceno2, Mauro Bisson3, Josh Romero3, Thorsten Kurth3, Massimiliano Fatica3, Eleftherios Garyfallidis4, Ariel Rokem5, Jason Crane1, and Sharmila Majumdar1; 1University of California, San Francisco, 2Janssen Pharmaceutical, 3NVIDIA Corporation, 4Indiana University, and 5University of Washington (United States) [view abstract]


12:30 – 2:00 PM Lunch

Tuesday 17 January PLENARY: Embedded Gain Maps for Adaptive Display of High Dynamic Range Images

Session Chair: Robin Jenkin, NVIDIA Corporation (United States)
2:00 PM – 3:00 PM
Cyril Magnin I/II/III

Images optimized for High Dynamic Range (HDR) displays have brighter highlights and more detailed shadows, resulting in an increased sense of realism and greater impact. However, a major issue with HDR content is the lack of consistency in appearance across different devices and viewing environments. There are several reasons, including varying capabilities of HDR displays and the different tone mapping methods implemented across software and platforms. Consequently, HDR content authors can neither control nor predict how their images will appear in other apps.

We present a flexible system that provides consistent and adaptive display of HDR images. Conceptually, the method combines both SDR and HDR renditions within a single image and interpolates between the two dynamically at display time. We compute a Gain Map that represents the difference between the two renditions. In the file, we store a Base rendition (either SDR or HDR), the Gain Map, and some associated metadata. At display time, we combine the Base image with a scaled version of the Gain Map, where the scale factor depends on the image metadata, the HDR capacity of the display, and the viewing environment.

Eric Chan, Fellow, Adobe Inc. (United States)


Eric Chan is a Fellow at Adobe, where he develops software for editing photographs. Current projects include Photoshop, Lightroom, Camera Raw, and Digital Negative (DNG). When not writing software, Chan enjoys spending time at his other keyboard, the piano. He is an enthusiastic nature photographer and often combines his photo activities with travel and hiking.

Paul M. Hubel, director of Image Quality in Software Engineering, Apple Inc. (United States)


Paul M. Hubel is director of Image Quality in Software Engineering at Apple. He has worked on computational photography and image quality of photographic systems for many years on all aspects of the imaging chain, particularly for iPhone. He trained in optical engineering at University of Rochester, Oxford University, and MIT, and has more than 50 patents on color imaging and camera technology. Hubel is active on the ISO-TC42 committee Digital Photography, where this work is under discussion, and is currently a VP on the IS&T Board. Outside work he enjoys photography, travel, cycling, coffee roasting, and plays trumpet in several bay area ensembles.

3:00 – 3:30 PM Coffee Break

High Performance Tomographic Reconstruction (T3)

Session Chair: Peng Chen, Japan National Lab (AIST) (Japan)
3:30 – 5:30 PM

Training end-to-end unrolled iterative neural networks for SPECT image reconstruction: A fast and memory efficient Julia toolbox (Invited), Zongyu Li, Yuni K. Dewaraja, and Jeff Fessler, University of Michigan (United States) [view abstract]


Fast GPU-based tomographic reconstruction with efficient data transfers between CPU, GPU, and NVMe SSDs (Invited), Viktor Nikitin, Argonne National Laboratory (United States) [view abstract]


Hierarchical communications for 3D image reconstruction with synchrotron light source and 24,576 GPUs (Invited), Mert Hidayetoglu; Stanford University and SLAC National Accelerator Laboratory (United States) [view abstract]


High-performance image reconstruction on GPU-accelerated supercomputers (Invited), Peng Chen1, Mohamed Wahib2, Xiao Wang3, Jintao Meng4, and Yusuke Tanimura1; 1Japan National Lab (AIST) (Japan), 2RIKEN Center for Computational Science (Japan), 3Oak Ridge National Laboratory (United States), and 4Shenzhen Institute of Advanced Technology, CAS (China) [view abstract]


5:30 – 7:00 PM EI 2023 Symposium Demonstration Session (in the Cyril Magnin Foyer)

Wednesday 18 January 2023

PANEL: High-Performance Computing in Imaging: from Academia to Industry (W1)

Panelists: Yuankai Huo, Vanderbilt University (United States); Yucheng Tang, NVIDIA Corporation (United States); and Xiao Wang, Oak Ridge National Laboratory (United States)
9:10 – 10:10 AM

10:00 AM – 3:30 PM Industry Exhibition - Wednesday (in the Cyril Magnin Foyer)

10:20 – 10:50 AM Coffee Break

AI Acceleration & System Design (W2)

Session Chair: Mohamed Wahib, RIKEN Center for Computational Science (Japan)
10:50 AM – 12:30 PM

TVM enabled automatic kernel generation for irregular GEMM optimization on ARM architectures (Invited), Du Wu1,2, Chen Zhuang2, Haidong Lan3, Wenxi Zhu3, Minwen Deng3, Peng Chen4, Mohamed Wahib5, Jintao Meng2, Bingqiang Wang6, Yanjie Wei2, Yi Pan2, and Shengzhong Feng7; 1Southern University of Science and Technology of China (China), 2Shenzhen Institue of Advanced Technology, Chinese Academy of Science (China), 3Tencent AI Lab, Shenzhen (China), 4Japan National Lab (AIST) (Japan), 5RIKEN Center for Computational Science (Japan), 6Peng Cheng Laboratory, Shenzhen (China), and 7National Supercomputer Center in Shenzhen, China (China) [view abstract]


Towards real-time formula driven dataset feed for large scale deep learning training, Edgar Josafat Martinez Noriega1 and Rio Yokota2; 1National Institute of Advanced Industrial Science and Technology and 2Tokyo Institute of Technology (Japan) [view abstract]


Algorithmic enhancements to data colocation grid frameworks for big data medical image processing (Invited), Shunxing Bao1 and Yuankai Huo2; 1Vanderbilt University and 2presenter only (United States) [view abstract]


Bridging the gap between high-performance computing and high-performance imaging applications, Mohamed Wahib, RIKEN Center for Computational Science (Japan) [view abstract]


12:30 – 2:00 PM Lunch

Wednesday 18 January PLENARY: Bringing Vision Science to Electronic Imaging: The Pyramid of Visibility

Session Chair: Andreas Savakis, Rochester Institute of Technology (United States)
2:00 PM – 3:00 PM
Cyril Magnin I/II/III

Electronic imaging depends fundamentally on the capabilities and limitations of human vision. The challenge for the vision scientist is to describe these limitations to the engineer in a comprehensive, computable, and elegant formulation. Primary among these limitations are visibility of variations in light intensity over space and time, of variations in color over space and time, and of all of these patterns with position in the visual field. Lastly, we must describe how all these sensitivities vary with adapting light level. We have recently developed a structural description of human visual sensitivity that we call the Pyramid of Visibility, that accomplishes this synthesis. This talk shows how this structure accommodates all the dimensions described above, and how it can be used to solve a wide variety of problems in display engineering.

Andrew B. Watson, chief vision scientist, Apple Inc. (United States)


Andrew Watson is Chief Vision Scientist at Apple, where he leads the application of vision science to technologies, applications, and displays. His research focuses on computational models of early vision. He is the author of more than 100 scientific papers and 8 patents. He has 21,180 citations and an h-index of 63. Watson founded the Journal of Vision, and served as editor-in-chief 2001-2013 and 2018-2022. Watson has received numerous awards including the Presidential Rank Award from the President of the United States.

3:00 – 3:30 PM Coffee Break

5:30 – 7:00 PM EI 2023 Symposium Interactive (Poster) Paper Session (in the Cyril Magnin Foyer)

5:30 – 7:00 PM EI 2023 Meet the Future: A Showcase of Student and Young Professionals Research (in the Cyril Magnin Foyer)

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