Special Issue on Deep Learning for Emerging Embedded Real-Time Image and Video Processing Systems

The main aim of the multimedia as related to image and video processing is to enable real-time image super resolution or a visually pleasing high resolution image based on low-resolution image sequences.  High resolution images are composed of higher pixel density with fine and more precise details as compared with low-resolution images or video. Many related applications, such as video surveillance, ultra-high definition TV, low-resolution face recognition, and remote image sensing are based on super-resolution techniques. These techniques have attracted high interest from both acemedia and industry, and currently is an active area of research in image and video processing.  
     Previously, conventional machine learning techniques, such as supervised and unsupervised learning, reinforcement learning, Bayes classifier, K means clustering, random forests, and decision trees, etc., have been utilized. Recently, the rapid advancements in deep learning or deep neural networks has shown a promising performance for high resolultion scenarios. There remain many research issues regarding the high resolution aspect. The objective of this special issue is to examine high resolution issues from the perspetive of deep learning for real-time super resolution image and video processing, including new objective functions, new architectures, large scale images, depth images, data acquisition, feature representation, knowledge understanding, and semantic modeling, types of corruption, and new applications. There still exists a gap between extracting representations (or knowledge) from high resoluion image and video data and their practical demands. 
     This special issue on “Deep Learning for Emerging Embedded Real-Time Image and Video Processing Systems” is intended to provide representative papers in the current state-of-the-art in the field of mobile embedded real-time image and video processing systems. Contributions to this special issue are solicited based on original and unpublished manuscripts that illustrate research results, projects, surveying works and industrial experiences dealing with theory and applications within the theme of Deep Learning for Emerging Embedded Real-Time Image and Video Processing Systems. Authors are encouraged to submit contributions related to any of the following or similar topics involving real-time processing or parallel computing: 

Topics of interest include, but are not limited to:

  • Supervised deep learning methods for real-time image and video embedded systems 
  • Real-time hybrid RGB and depth image and video super resolution with deep learning
  • Deep learning for real-time large scale embedded systems
  • Real-time hardware and systems for deep learning of image and video data 
  • New image and video databases for real-time deep learning 
  • Acceleration of deep learning for embedded systems 
  • Real-time data visualization patterns, query processing and analysis of big image and video data
  • Business Intelligence for deep learning for real-time image and video systems
  • Real-time deep learning framework for big image and video data
  • Modern technologies for real-time deep learning in embedded systems 
  • 2D/3D image and video data understanding for real-time deep learning 
  • Real-time tools and applications for medicine and healthcare data (e.g. clustering, storing, ranking, hashing, and retrieval)
  • Real-time knowledge integration of multi-modal data through transfer learning and deep neural network

Important Dates:
Manuscript due:                             January 1, 2021 
Acceptance/rejection notification: March 1, 2021
2nd round check:                           May 1, 2021
Final manuscript due:                     July 1, 2021

Guest Editors:
Gwanggil Jeon, Incheon National University, Korea, gjeon@inu.ac.kr
Ernesto Damiani, Khalifa University, UAE, ernesto.damiani@kustar.ac.ae
Marcelo Keese Albertini, Universidade Federal de Uberlândia, Brazil, albertini@ufu.br
Abdellah Chehri, Université du Québec à Chicoutimi, Canada, Abdellah_Chehri@uqac.ca

Authors from academia and industry working in the above research areas are invited to submit original manuscripts that have not been published and are not currently under review by other journals or conferences. All potential authors are requested to volunteer as reviewers in the peer‐review process for manuscripts submitted for this special issue.
All submitted manuscripts being eligible with respect to the scope of JRTIP and the focus of this special issue will be peer-reviewed based on their originality, presentation, and novelty, as well as their suitability to the special issue. Previously published conference papers should be clearly stated by the authors and an explanation should be provided how such papers have been extended to be considered for this special issue. 

Paper submissions for the special issue should follow the submission format and guidelines of the journal (https://www.springer.com/journal/11554/submission-guidelines). Note that there is a page limit of 12 pages (double column format).

During the submission procedure in Editorial Manager (https://www.editorialmanager.com/rtip/default.aspx), authors should select ‘SI: Deep Learning for Emerging Embedded Real-Time Image and Video Processing Systems' at the submission step 'Additional Information'.

Prior to sending full paper submissions, it is highly recommended to query the appropriateness of submissions with a 100-200 word abstract by contacting the guest editor with the following contact information:
Gwanggil Jeon, Incheon National University, Korea, gjeon@inu.ac.kr