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IEEE Signal Processing Society Blog


The SPS blog aims to raise awareness about signal processing and Society-related topics to a general interest audience in an engaging, informal, and non-technical way. If you're interested in contributing to the SPS blog, please contact the SPS Blog Team at sps-blog@ieee.org for more information.

16 Dec.

IEEE SustainTech Leadership Forum Draws World’s Top Technical and Sustainability Experts

By: 
IEEE Signal Processing Society

The new global thought leadership event brought together industry movers and shakers to discuss the future of Buildings and Factories in the Built Environment.

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22 Nov.

Occlusion-Aware Human Mesh Model-Based Gait Recognition

By: 
Prof. Chi Xu

An occlusion-aware model for gait video processing uses SMPL-based human mesh models and machine learning to achieve superior recognition in challenging surveillance videos.

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13 Nov.

FSIM: A Feature Similarity Index for Image Quality Assessment

By: 
Dr. Lin Zhang

We propose a novel low-level feature similarity (FSIM) induced FR IQA metric, namely, FSIM. FSIM can measure image quality automatically and consistently with human perception.

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05 Nov.

Physics Makes Black-box Deep Learning Models Transparent

By: 
Prof. Zicheng Liu

Electromagnetic inverse scattering problems (ISPs) are crucial in noninvasive imaging but challenging due to nonlinearity and computational costs. This blog explores machine learning-based ISP solvers with physics-guided loss functions, emphasizing the role of near-field priors and multiple-scattering effects. Numerical experiments highlight the advantages and limitations of these approaches.

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09 Sep.

Optimize Your Signal Processing with Bayesian Optimization

By: 
Richard Cornelius Suwandi

Explore how Bayesian optimization enhances signal processing applications by providing efficient algorithm design solutions in the signal processing toolbox.

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11 Jul.

Devising Transformers as an Autoencoder for Unsupervised Multivariate Time Series Imputation

By: 
Dr. Aykut Koç

Inspired by the capabilities of transformer models, we introduce a novel method named Multivariate Time-Series Imputation with Transformers (MTSIT). This entails an unsupervised autoencoder model featuring a transformer encoder, leveraging unlabeled observed data for simultaneous reconstruction and imputation of multivariate time-series.

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11 Apr.

Unlocking Real-Time 3D Imaging with Single-Photon LiDAR in Challenging Environments

By: 
Dr. Abderrahim Halimi

Our method overcomes 3D underwater imaging challenges by offering high-frame-rate video 3D imaging (>100 fps), providing uncertainty measures for estimates, and extending applicability to various obscurant media imaging.

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01 Apr.

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

By: 
Dr. Qiuqiang Kong

Pretrained audio neural networks (PANNs) are trained on 5800 hours of AudioSet data that can be used to recognize hundreds of sound types in the natural world.

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12 Feb.

Deep Learning for All-in-Focus Imaging

By: 
Dr. Qian Huang

Focus stacking is an effective approach to extending the depth of field of a camera, yet is challenging with regard to 1) controlling focal planes in forming a stack and 2) fusing the focal stack into composites free from defocusing, i.e., all-in-focus. We propose a deep learning all-in-focus imaging pipeline as a novel solution for focus stacking.

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01 Dec.

Underwater Image Enhancement via a Fast yet Effective Traditional Method

By: 
Weidong Zhang, Peixian Zhuang, Hai-Han Sun, Guohou Li, Sam Kwong, Chongyi Li

Addressing underwater image challenges, our method MLLE enhances color, contrast, and details efficiently. Outperforming competitors, it processes 1024×1024×3 images in under 1s on a single CPU. Experiments show improved underwater image segmentation, keypoint detection, and saliency detection.

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