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®

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:

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).