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While snapshot hyperspectral cameras are cheaper and faster than imagers based on pushbroom or whiskbroom spatial scanning, the output imagery from a snapshot camera typically has different spectral bands mapped to different spatial locations in a mosaic pattern, requiring a demosaicing process to be applied to generate the desired hyperspectral image with full spatial and spectral resolution.

The IEEE SPS congratulates the following SPS members who will receive the Society’s prestigious awards during ICASSP 2025 in India.

22 Jan

SPS-DSI (DEGAS) Webinar: Demystifying and Mitigating Unfairness for Learning over Graphs

Date: 22 January 2025
Time: 10:00 AM ET (New York time)
Presenter(s): Yanning Shen

01 Nov 04 Nov

(SiPS 2025) 2025 IEEE International Workshop on Signal Processing Systems

Date: 1-4 November 2025
Location: Hong Kong

Conference Paper Submission Deadline: 
01 June 2025
12 Oct 15 Oct

(WASPAA 2025) 2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Date: 12-15 October 2025
Location: Lake Tahoe, CA, USA

Conference Paper Submission Deadline: 
30 April 2025

The IEEE Signal Processing Society (SPS) will hold a Special Election to fill the vacant President-Elect position, which will open on 6 February 2025 and close on 26 March 2025.

We consider the problem of recovering off-the-grid spikes from linear measurements in the context of Single Molecule Localization Microscopy (SMLM). State of the art model-based methods such as Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Projected Gradient Descent (PGD) have been shown to successfully recover those signals. 

Single-satellite geolocation achieves effective localization of ground electromagnetic interference (EMI) signals with a low cost compared to the multi-satellite counterparts. In such systems, the Doppler and Doppler rate are commonly exploited to extract the information of the ground EMI sources and the constrained Unscented Kalman filter (cUKF) is found effective to provide instantaneous EMI locations over time. 

Scene-Text Visual Question Answering (STVQA) is a comprehensive task that requires reading and understanding the text in images to answer the question. Existing methods of exploring the vision-language relationships between questions, images, and scene text have achieved impressive results. However, these studies heavily rely on auxiliary modules, such as external OCR systems and object detection networks, making the question-answering process cumbersome and highly dependent.

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