scholarly journals Total Variation Based Neural Network Regression for Nonuniformity Correction of Infrared Images

Symmetry ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 157 ◽  
Author(s):  
Rui Lai ◽  
Gaoyu Yue ◽  
Gangxuan Zhang
Keyword(s):  
Neural Network ◽  
Total Variation ◽  
Infrared Images ◽  
Nonuniformity Correction

scholarly journals Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 612 ◽  
Author(s):  
Xianzhong Jian ◽  
Chen Lv ◽  
Ruzhi Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infrared Imaging ◽  
Imaging System ◽  
Infrared Images ◽  
Nonuniformity Correction ◽  
Training Images ◽  
Quantitative Measurements ◽  
Complex Training ◽  
Simulated Training

The fixed-pattern noise (FPN) caused by nonuniform optoelectronic response limits the sensitivity of an infrared imaging system and severely reduces the image quality. Therefore, nonuniform correction of infrared images is very important. In this paper, we propose a deep filter neural network to solve the problems of network underfitting and complex training with convolutional neural network (CNN) applications in nonuniform correction. Our work is mainly based on the idea of deep learning, where the nonuniform image noise features are fully learned from a large number of simulated training images. The network is designed by introducing the filter and the subtraction structure. The background interference of the image is removed by the filter, so the learning model is gathered in the nonuniform noise. The subtraction structure is used to further reduce the input-to-output mapping range, which effectively simplifies the training process. The results from the test on infrared images shows that our algorithm is superior to the state-of-the-art algorithm in visual effects and quantitative measurements, providing a new method for deep learning in nonuniformity correction of single images.

Adaptive Nonuniformity Correction for Infrared Images Based on Temporal Moment Matching

2015 ◽  
Vol 35 (4) ◽  
pp. 0410003
Author(s):  
冷寒冰 Leng Hanbing ◽  
易波 Yi Bo ◽  
谢庆胜 Xie Qingsheng ◽  
唐利孬 Tang Li′nao ◽  
宫振东 Gong Zhendong
Keyword(s):  
Moment Matching ◽  
Infrared Images ◽  
Nonuniformity Correction ◽  
Temporal Moment

scholarly journals Fire detection with infrared images using cascaded neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261989543
Author(s):  
Li Deng ◽  
Qian Chen ◽  
Yuanhua He ◽  
Xiubao Sui ◽  
Quanyi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Stage ◽  
Infrared Camera ◽  
Fire Detection ◽  
Image Texture ◽  
Infrared Images ◽  
Detection Technology ◽  
Civil Aircraft

The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s (FAA) statistics, the false alarm rate is as high as 99%. 1 In the cargo of civil aircraft, the traditional photoelectric detection technology cannot effectively distinguish interference particles from smoke particles. Since the video smoke detection technology has proven to be reliable in many large scenarios, a deep learning method of image processing for fire detection is proposed. The proposed convolutional neural network is constructed of front end network and back end network cascaded with the capsule network and the circularity computation for the dynamic infrared fire image texture extraction. In order to accurately identify whether there is a fire in the area and give the kind of burning substances, a series of fuels are selected, such as n-heptane, cyclohexane, and carton for combustion reaction, and infrared camera is used to take infrared images of all fuel combustion. Experimental results show that the proposed method can effectively detect fire at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

Spatially adaptive hybrid variational model for temperature-dependent nonuniformity correction of infrared images

2020 ◽  
Vol 59 (12) ◽  
Author(s):  
Zhenhua Li ◽  
Guili Xu ◽  
Yuehua Cheng ◽  
Zhengsheng Wang ◽  
Quan Wu ◽  
...  
Keyword(s):  
Variational Model ◽  
Infrared Images ◽  
Temperature Dependent ◽  
Nonuniformity Correction ◽  
Spatially Adaptive

Low-observable targets detection for autonomous vehicles based on dual-modal sensor fusion with deep learning approach

2019 ◽  
Vol 233 (9) ◽  
pp. 2270-2283
Author(s):  
Keke Geng ◽  
Wei Zou ◽  
Guodong Yin ◽  
Yang Li ◽  
Zihao Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Complex Environments ◽  
Infrared Images ◽  
Night Time ◽  
Perception System

Environment perception is a basic and necessary technology for autonomous vehicles to ensure safety and reliable driving. A lot of studies have focused on the ideal environment, while much less work has been done on the perception of low-observable targets, features of which may not be obvious in a complex environment. However, it is inevitable for autonomous vehicles to drive in environmental conditions such as rain, snow and night-time, during which the features of the targets are not obvious and detection models trained by images with significant features fail to detect low-observable target. This article mainly studies the efficient and intelligent recognition algorithm of low-observable targets in complex environments, focuses on the development of engineering method to dual-modal image (color–infrared images) low-observable target recognition and explores the applications of infrared imaging and color imaging for an intelligent perception system in autonomous vehicles. A dual-modal deep neural network is established to fuse the color and infrared images and detect low-observable targets in dual-modal images. A manually labeled color–infrared image dataset of low-observable targets is built. The deep learning neural network is trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in complex environments. The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.

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