Call for Papers Deadlines
» Journal-first (JIST & JPI)
5 April
» Conference
» Presentation Only
26 May
26 Jul
Acceptance Notification
» Conference 30 June 
» Journal-first (JIST & JPI)
7 July 
Preliminary Program Released & Registration Opens mid-July
Final Manuscripts Due
» Journal-first by 12 July
» Conference
23 Aug
Early Registration Ends
Conference Begins
20 Sept


Conference Committee

General Chair

Sophie Triantaphillidou, University of Westminster (UK)

Program Committee

Seyed Ali Amirshahi, NTNU (Norway)
Simone Bianco, University Milano-Biccoca (Italy)
Sebastian Bosse, Fraunhofer Heinrich Hertz Institute HHI (Germany)
Alexander Braun, University of Applied Sciences (Germany)
Peter Burns, Burns Digital Imaging (US)
Christy Fernandez-Cull, Lyft / MIT (US)
Valentina Donzella, University of Warwick (UK)
Chaker Larabi, University of Poitier (France)
Yi-Tun Lin, University of East Anglia (UK)
Michal Mackiewicz, University of East Anglia (UK)
Ronald Mueller, Vision Markets GmbH (Germany)

Marius Pedersen, NTNU (Norway)
Jonathan Phillips, Google (US)
Alexandra Psarrou, University of Westminster (UK)
Raquel Gil Rodríguez, Justus Liebig University Giessen (Germany)
Raimondo Schettini, University Milano-Biccoca (Italy)
Giorgio Trumpy, University Zurich (Switzerland)
Maria Vanrell, Universitat Autònoma de Barcelona (Spain)
Barbara Villarini, University of Westminster (UK)
Stephen Westland, Leeds Universty (UK)

Steering Committee
Michael S. Brown, York University (Canada) 
Graham Finlayson, University of East Anglia (UK) 
Susan Farnand, Rochester Institute of Technology (US)
Rafal Mantiuk, University of Cambridge (UK) 
Javier Vázquez Corral, Universitat Autònoma de Barcelona (Spain) 

Organization Lead

Suzanne Grinnan, IS&T (US)

Join us Online for LIM 2021: 20-22 September

About the London Imaging Meeting

The London Imaging Meeting (LIM) is a yearly topic-based conference focused on the future of imaging science. The theme for 2021 is “Imaging for Deep Learning”. Proposals for the 2022 theme are being accepted (see below).

Imaging for Deep Learning Program Announced

LIM 2021 akes place over 3 days, with short courses held 20 September and the technical program held 21-22 September.

On this page


ANNOUNCEMENT: LIM is currently accepting presentation-only poster submissions in the topic areas listed below. Deadline 26 July 2021 View Details

The theme of LIM 2021 is IMAGING FOR DEEP LEARNING. We are interested in two aspects of deep-learning and imaging science:

  • Imaging science to improve deep learning performance
  • Deep learning methods intended to improve imaging systems performance
We seek to attract new research in areas relating to the ways imaging system design, processes, their performance, and outputs affect deep neural network (DNN) performance.
We are also interested in deep learning methods to improve imaging: how deep neural networks are used to optimize imaging system design, predict performance, and output image quality.

The conference is designed to promote interaction between the imaging science and deep learning communities. It includes research, keynote, and focus presentations.


Topics of interest include, but are not limited to:

  • Impact of imaging system design on DNN performance
  • Imaging system optimization for improved DNN performance
  • Imaging system modelling for data augmentation for DNN training
  • Metrification of imaging performance for DNN input
  • DNNs for metrification of imaging systems
  • DNNs for optimization of imaging performance
  • DNNs for image quality prediction
  • DNN components for imaging systems
  • Domain adaptation of DNNs for imaging systems

In their papers, we encourage authors to speculate on how their research in imaging science will impact the field of deep learning in the future and vice versa. We welcome participation of scientists and engineers from academia and industry, and strongly encourage contributions from graduate students. LEARN MORE

We are pleased to announce our following distinguished LIM 2021 speakers. See program details.

Soft-Prototyping Camera Designs for Autonomous Driving

Dr. Joyce E. Farrell, executive director
Stanford University Center for Image Systems Engineering (SCIEN)

Camera Metrics for Autonomous Vision
(working title)
Dr. Robin Jenkin, principal image quality engineer


Using imaging data for efficient colour design
Stephen Westland, professor of colour science and technology / co-founder
University of Leeds / Colour Intelligence Ltd.


Deep learning in image quality assessment: past, present, and what lies ahead
Seyed Ali Amirshahi, associate professor
Norwegian University of Science and Technology (NTNU)

Image understanding for color constancy and vice versa
Simone Bianco, associate professor of Computer Science
Università degli Studi di Milano-Bicocca

The data conundrum: compression of automotive imaging data and deep neural network based perception
Valentina Donzella, associate professor, Intelligent Vehicles Group
University of Warwick

Mitigating limitations of deep neural networks for imaging systems
Ray Ptucha, computational display technology leader

Generative models for image data augmentation
Jonas Unger, professor
Linköping University

Online 2021

LIM2021 will be held online. The meeting's online logistics are being organized and run by the Institute of Physics (IOP). As details become available, they will be posted here.

LIM 2022

The LIM Steering Committee seeks inquiries from colleagues/groups interested in proposing a theme and/or chairmanship for the 2022 London Imaging Meeting. Inquiries/proposals may be directed to

Funding for the conference keynotes and proceedings is supported by EPSRC.

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