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machine learning for signal processing

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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An Echo in Time: Tracing the Evolution of Beamforming Algorithms

By: 
Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov, and Robert W. Heath, Jr.

Beamforming is a widely used signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction.

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Model-Driven Deep Learning for MIMO Detection

By: 
Dr. Hengtao He

In this blog, we investigate the model-driven deep learning for multiple input-multiple output (MIMO) detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters.

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