[visionlist] Speical issue on The Deep Learning in Computational Photography

Lixin Fan lixin.fan01 at gmail.com
Sat Sep 22 06:48:19 -05 2018

Signal Processing: Image Communication


The Deep Learning in Computational Photography

The Computational Photography is a new and rapidly developing subject. By
integrating a variety of technologies such as digital sensors, optical
systems, intelligent lighting, signal processing, computer vision, and
machine learning, computational photography aims at improving the
traditional imaging technology, in which an image is formed directly at
sensors. The joint force Computational Photography enhances and extends the
data acquisition capabilities of traditional digital cameras, and captures
the full range of real-world scene information.

With rapidly advancing hardware, some studies use highly curved image
sensors to improve optical performance, some try to optimize the micro-lens
array-based light field camera systems, the others propose fundamentally
new imaging modalities for depth cameras. Despite that new sensing
technologies are able to provide better image quality even richer
information, cost constraints often limit large scale applications of such
technologies.  Instead, learning-based computational photography techniques
demonstrate a potential capability of enhancing the camera systems without
requiring a significate upgrade of hardware.  Recently, deep neural
networks have shown their superior performance in the imaging computation.
They can either learn a complex imaging mechanism in a low-light
environment, detect objects to strengthen the focus function and depth
estimation, or enhance the degraded images captured in bad work conditions,
just to name a few.

The objective of this special issue is to provide a forum for researchers
to share their recent progresses on deep learning for computation
photography. Papers could cover broad aspects from both theoretical and
engineering perspectives, including DNN techniques for modeling, DNN
algorithms for image reconstruction, and novel DNN designs for
computational imaging in various spectral regimes, such as optical,
multi-spectrum, ultrasound, microwave regimes, and so on. Contributions are
also welcome concerning applications using computational photography, from
fundamental science to applied research.

Potential topics include, but are not limited to:

   - Computational imaging methods and models
   - Computational illumination
   - Computational image processing
   - Multi-spectral imaging, SAR imaging, medical imaging and their
   - Post-processing in computational photography
   - Degraded image enhancement
   - Aesthetics captioning
   - Image recovery from compressed sensing
   - Image generation through domain learning
   - Applications, including natural, medical, remote sensing research.

Tentative schedule:
Paper submission due: January 18, 2019
First notification: March 31, 2019
Revision: May 31, 2019
Final decision: June 30, 2019
Tentative Publication date: August 2019

Guest Editors:

   1. Xuefeng Liang, Professor, Xidian University, China (
   xliang at xidian.edu.cn) http://web.xidian.edu.cn/xliang/en/index.html &
   Visiting Professor, Kyoto University, Japan (xliang at i.kyoto-u.ac.jp )
   Dr. Xuefeng Liang is professor at School of Artificial Intelligent,
   Xidian University. During 2010 – 2018, he was an associate professor at
   Department of Intelligence Science and Technology, Graduate School of
   Informatics, Kyoto University. His research areas of interests include
   visual perception & cognition (psychology), computer vision and intelligent
   algorithms. Dr. Liang has published 59 international journals and
   conference papers, and received “Academic Award of Governors of Kyoto
   Prefecture” in 2017 and other 3 outstanding research/papers awards in past
   5 years. He is on the editorial board of two international journals, has
   chaired 6 international conferences/workshops, and served as TCP members
   for 6 international conferences. Before joining Kyoto University in 2010,
   Dr. Liang was a Research Assistant with the University College of London
   and with the Queen Mary University of London.
   2. Lixin Fan, Principal Scientist, Nokia Technologies,lixin.fan at nokia.com
   <%20lixin.fan at nokia.com> ,
   Dr Lixin Fan is a principal scientist at Nokia Technologies. His
   research areas of interests include Machine learning & deep learning,
   Computer vision & pattern recognition, Image and video processing, 3D big
   data processing, data visualization & rendering, Augmented and virtual
   reality, Mobile ubiquitous and pervasive computing and Intelligent
   human-computer interface.   Dr Fan is the (co-)author of more than 50
   international journal & conference publications, and the (co-)inventor of
   dozens of granted and pending patents filed in US, Europe and China. Dr Fan
   also co-organized workshops held jointly with CVPR, ICCV, ACCV, ICPR, ICME
   and ISMAR.  Before joining Nokia in 2004, Dr Fan was affiliated with Xerox
   Research Center Europe and his research work included the well recognized
   Bag of Keypoints method for image categorization.
   3. Chee Seng Chan, Associate Professor, University of Malaya,
   cs.chan at um.edu.my,
   Dr. Chee Seng Chan is an associate professor at Department of Artificial
   Intelligence, Faculty of Computer Science and Information Technology,
   University of Malaya. His research areas of interests include computer
   vision, image processing and fuzzy sets. Dr. Chan has published more than
   50 international journals and conference publications. He was the founding
   chair of IEEE Computational Intelligence Society (Malaysia chapter), and
   recipients of few prestige awards such as Young Scientist by Academy
   Science Malaysia in 2016, Hitachi Fellowships in 2012, as well as the Top
   100 British Young Engineers in 2010. He is a senior member of IEEE and a
   chartered engineer.

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