© 2019

Night Vision Processing and Understanding


Table of contents

  1. Front Matter
    Pages i-xvi
  2. Lianfa Bai, Jing Han, Jiang Yue
    Pages 1-15
  3. Lianfa Bai, Jing Han, Jiang Yue
    Pages 175-199
  4. Lianfa Bai, Jing Han, Jiang Yue
    Pages 235-266

About this book


This book systematically analyses the latest insights into night vision imaging processing and perceptual understanding as well as related theories and methods. The algorithm model and hardware system provided can be used as the reference basis for the general design, algorithm design and hardware design of photoelectric systems. Focusing on the differences in the imaging environment, target characteristics, and imaging methods, this book discusses multi-spectral and video data, and investigates a variety of information mining and perceptual understanding algorithms. It also assesses different processing methods for multiple types of scenes and targets.

Taking into account the needs of scientists and technicians engaged in night vision optoelectronic imaging detection research, the book incorporates the latest international technical methods. The content fully reflects the technical significance and dynamics of the new field of night vision. The eight chapters cover topics including multispectral imaging, Hadamard transform spectrometry; dimensionality reduction, data mining, data analysis, feature classification, feature learning; computer vision, image understanding, target recognition, object detection and colorization algorithms, which reflect the main areas of research in artificial intelligence in night vision.

The book enables readers to grasp the novelty and practicality of the field and to develop their ability to connect theory with real-world applications. It also provides the necessary foundation to allow them to conduct research in the field and adapt to new technological developments in the future.


Night Vision Multispectral Imaging Data Mining Computer Vision Image Understanding Data Analysis Feature Learning Dimensionality Reduction Target Recognition Feature Classification Object Detection Hadamard Transform Spectrometry Colorization Algorithm Artificial Intelligence

Authors and affiliations

  1. 1.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.National Key Laboratory of Transient PhysicsNanjing University of Science and TechnologyNanjingChina

About the authors

Lianfa Bai: His interests include photoelectron imaging, multispectral imaging, image processing and computer vision, and intelligent applications of spectral imaging. He has also pursued unique research on low level light visible infrared (near-infrared, medium-wave infrared, long-wave infrared) imaging and understanding. He has published more than 130 relevant papers, including in Optics Letters, IEEE Transactions, and PLOS ONE.

Jing Han: Her research is mainly based on system imaging characteristics, studying spectral data mining, visual modelling and optimization, and non-training/small sample training classification to improve the computational efficiency and robustness of multidimensional images, and to promote the practicality of multi-source multispectral imaging systems.

Jiang Yue: He is currently working on new technologies to boost the SNR of high-dimension data, including acquisition methods, and data denoising algorithms. In particular he is dealing with two problems: developing high SNR coding snapshot measurements and finding reversible denoising transformations. He and his co-operators have published more than 15 relevant papers, including in Optics Letters, IEEE Transactions on Image Processing, and Applied Physics B.

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