Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing

2020 ◽  
pp. 475-491
Author(s):  
Tongyao Pang ◽  
Yuhui Quan ◽  
Hui Ji
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Image Recovery

A Brain–Computer Interface Framework Based on Compressive Sensing and Deep Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 90-96
Author(s):  
Ritu Ranjan Shrivastwa ◽  
Vikramkumar Pudi ◽  
Chen Duo ◽  
Rosa So ◽  
Anupam Chattopadhyay ◽  
...  
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3702 ◽  
Author(s):  
Chiman Kwan ◽  
Bryan Chou ◽  
Jonathan Yang ◽  
Akshay Rangamani ◽  
Trac Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Compressive Sensing ◽  
Target Tracking ◽  
Low Power Consumption ◽  
Coded Aperture ◽  
Learning Approach ◽  
Residual Network ◽  
Practical Applications ◽  
Measurement Domain

Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.

Compressive sensing image recovery using dictionary learning and shape-adaptive DCT thresholding

2019 ◽  
Vol 55 ◽  
pp. 60-71 ◽  
Author(s):  
Dong Du ◽  
Zhibin Pan ◽  
Penghui Zhang ◽  
Yuxin Li ◽  
Weiping Ku
Keyword(s):  
Compressive Sensing ◽  
Dictionary Learning ◽  
Image Recovery

scholarly journals LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

2020 ◽  
Author(s):  
Yu Wang ◽  
Jie Yang ◽  
Miao Liu ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Scaling Factor ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Performance Loss ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
Large Model

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

Violent crowd behavior detection using deep learning and compressive sensing

2019 ◽  
Author(s):  
Mingliang Gao ◽  
Jun Jiang ◽  
Lixiu Ma ◽  
Shuwen Zhou ◽  
Guofeng Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Crowd Behavior ◽  
Behavior Detection

ROI-CSNet: Compressive sensing network for ROI-aware image recovery

2019 ◽  
Vol 78 ◽  
pp. 113-124 ◽  
Author(s):  
Zhifu Zhao ◽  
Xuemei Xie ◽  
Chenye Wang ◽  
Siying Mao ◽  
Wan Liu ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Image Recovery
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