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 particularhe 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.

Illustrates the potential of this powerful hyperspectral imaging and high-dimensional night vision data processing

Helps readers learn how to use spectral information in the visible and infrared waveband to solve challenging problems in real-life applications and discover how general image processing is connected to night vision imaging

Provides a comprehensive discussion of data mining and feature learning in state-of-the-art night vision imaging theory and methods

Describes the development of the international status and the latest results in the field, and offers an overview of research in multi-source night vision image processing

Explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs

Presents the intrinsic ideas behind spectral feature selection and understanding, and demonstrates how to derive theoretical foundations, find connections to other popular algorithms, and construct practical systems