SPS Newsletter Article

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. 

Many problems encountered in sensing and imaging can be formulated as estimating a low-rank object from incomplete, and possibly corrupted, linear measurements; prominent examples include matrix completion and tensor completion. 

The 2022 International Conference on Acoustics, Speech, & Signal Processing (ICASSP) invites proposals for its Signal Processing Grand Challenges (SPGC) program. ICASSP is the IEEE Signal Processing Society’s flagship conference targeting signal processing and its applications.

Adaptive (i.e., data-driven) methods have become very popular these last decades. Among the existing techniques, the empirical mode decomposition has proven to be very efficient in extracting accurate time-frequency information from non-stationary signals.

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. 

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.

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.

Satoshi Nakamura received his B.S. degree in electronics engineering from Kyoto Institute of Technology, Kyoto, in 1981. He received his Ph.D. in informatics from Kyoto University in 1992. He was the Department Head and Director of ATR Spoken Language Communication Research Laboratories, Kyoto, Japan in the period of 2000-2008.

This webinar will demonstrate how deep learning can solve difficult communication problems that prior approaches often fail with two case studies. The first half will discuss a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. 

We study the dual problem of image super-resolution (SR), which we term image compact-resolution (CR). Opposite to image SR that hallucinates a visually plausible high-resolution image given a low-resolution input, image CR provides a low-resolution version of a high-resolution image, such that the low-resolution version is both visually pleasing and as informative as possible compared to the high-resolution image.