SPS Webinar: 10 December 2021: Image Fusion with Convolutional Sparse Representation

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

News and Resources for Members of the IEEE Signal Processing Society

SPS Webinar: 10 December 2021: Image Fusion with Convolutional Sparse Representation

Upcoming SPS Webinar!

Title: Image Fusion with Convolutional Sparse Representation
Date: 10 December 2021
Time: 10:00 AM ET (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Yu Liu

Based on the IEEE Xplore® article: Image Fusion with Convolutional Sparse Representation
Published: IEEE Signal Processing Letters, October 2016
Download: Original article will be made freely available for download for 48 hours from the day of the webinar, on IEEE Xplore®

 

Register for the Webinar

 

Abstract:

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion during the last decade. However, due to the patch-based manner adopted in standard SR models, most existing SR-based image fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to mis-registration, while these two issues are of great concern in image fusion. We introduce a recently emerged signal decomposition model known as convolutional sparse representation (CSR) into image fusion to address this problem, which is motivated by the observation that the CSR model can effectively overcome the above two drawbacks. A CSR-based image fusion framework is proposed for multi-focus image fusion and multi-modal image fusion. In addition, we also extend the CSR model from single-component to multi-component for image fusion via the morphological component analysis (MCA) technique. Experimental results demonstrate that the proposed CSR-based fusion methods clearly outperform conventional SR-based methods in terms of both objective assessment and visual quality.
 


Biography:

Yu Liu

Dr. Yu Liu (Member, IEEE) received the B.S. degree in automation from University of Science and Technology of China, Hefei, China, in 2011, and the Ph.D. degree in control science and engineering from University of Science and Technology of China, Hefei, China, in 2016.

He is currently an associate professor in the Department of Biomedical Engineering at Hefei University of Technology. His research interests include image fusion, image restoration, image segmentation and biomedical signal/image processing. He has published over 50 scientific papers in prestigious peer-reviewed journals and conferences, in which 9 journal articles have been selected as ESI Highly Cited Papers.

Dr. Liu is serving as an Editorial Board Member for Information Fusion. Dr. Liu’s awards include IEEE Instrumentation and Measurement Society Andy Chi Best Paper Award (2020) and IET Image Processing Premium (Best Paper) Award (2017). Dr. Liu was identified as a Highly Cited Chinese Researcher by Elsevier (2020).

 

SPS on Twitter

  • DEADLINE EXTENDED: The 2023 IEEE International Workshop on Machine Learning for Signal Processing is now accepting… https://t.co/NLH2u19a3y
  • ONE MONTH OUT! We are celebrating the inaugural SPS Day on 2 June, honoring the date the Society was established in… https://t.co/V6Z3wKGK1O
  • The new SPS Scholarship Program welcomes applications from students interested in pursuing signal processing educat… https://t.co/0aYPMDSWDj
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now seeking submissions for a Special… https://t.co/NPCGrSjQbh
  • Test your knowledge of signal processing history with our April trivia! Our 75th anniversary celebration continues:… https://t.co/4xal7voFER

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel