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SPS Newsletter Article

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

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. 

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Member Highlights: Ngai-Man (Man) Cheung

I received my Ph.D. degree from University of Southern California (USC), Los Angeles, CA. Currently I am an Associate Professor and Associate Head of Information Systems Technology and Design (ISTD), Singapore University of Technology and Design (SUTD). I have been an active researcher in the field of Image Processing and Computer Vision. My research has resulted in 14 U.S. patents granted with several pending. Two of my inventions have been licensed to companies. 

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Call for Papers DSLW 2022: 2022 IEEE Data Science and Learning Workshop

The DSLW team is inviting you to submit regular papers to the 2022 IEEE Data Science & Learning Workshop (DSLW 2022), a workshop organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.

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Call for Papers DSLW 2022: 2022 IEEE Data Science and Learning Workshop

The DSLW team is inviting you to submit regular papers to the 2022 IEEE Data Science & Learning Workshop (DSLW 2022), a workshop organized by the IEEE Signal Processing Society (supported by the SPS Data Science Initiative). The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.

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