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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.

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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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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Deep CNN-Based Channel Estimation Using 3D Channel Correlation

By: 
Peihao Dong

Millimeter wave (mmWave) communications provide a promising solution to meet the proliferating demand for high data rate because of large bandwidth. The current “boomingly” deployed fifth generation communication system (5G) has not actually touched the dominant frequency band of mmWave and thus can hardly enjoy its merit on dramatically boosting transmission rate, which motivates us to conduct research on the ultimate implementation of mmWave communications.

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Coarse-to-Fine CNN for Image Super-Resolution

By: 
Chunwei Tian, Yong Xu, Wangmeng Zuo, Bob Zhang, Lunke Fei, Chia-Wen Lin

A coarse-to-fine SR CNN (CFSRCNN) consisting of a stack of feature extraction blocks (FEBs), an enhancement block (EB), a construction block (CB) and, a feature refinement block (FRB) is proposed to learn a robust SR model.

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Collaborative Cloud and Edge Mobile Computing in C-RAN Systems

By: 
Seok-Hwan Park, Seongah Jeong, Jinyeop Na, Osvaldo Simeone, Shlomo Shamai

To handle the various types of tasks in the upcoming cellular services, we can design the system with both cloud and edge computing capabilities, where the computational tasks can be partially offloaded to the ENs and the CP.

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Revitalizing Underwater Image Enhancement in the Deep Learning Era

By: 
Dr. Chongyi Li

Underwater image enhancement has drawn considerable attention in both image processing and underwater vision. Due to the complicated underwater environment and lighting conditions, enhancing underwater image is a challenging problem. 

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10 Mar.

How can we make cameras smarter to better analyze humans?

By: 
Dr. Joe (Zhou) Ren

This blog describes 4 computer vision algorithms for better human analysis, that understand human hand, gesture, pose, and action from various input modalities.

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Deep-learning-based audio-visual speech enhancement

By: 
Dr. Daniel Michelsanti

We all experienced the discomfort of communicating with our friends at a cocktail party or in a pub with loud background music. When difficult acoustic scenarios like these occur, we tend to rely on several visual cues, such as lips and mouth movement of the speaker, in order to understand the speech of interest.

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PANNs: Large-scale pretrained audio neural networks for audio pattern recognition

By: 
Dr. Qiuqiang Kong

Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. In this blog, we introduce pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature.

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