Academic Areas: Multi-spectral Image Processing, Target Detection and Recognition，Hardware Architecture and Embedded System for Real-time Image/video Processing .
Yan Luxin is an associate professor of the Department of Aircraft Navigation and Guidance, School of Automation. He is a member of State Key Laboratory of Image Processing and Intelligent Control, and the Science and Technology on Multispectral Information Processing Laboratory. His current research interests include multi-spectral image processing, target detection and recognition，hardware architecture and embedded system for real-time image/video processing and so on. He has published over 15 papers in the important international journals and conference such IEEE T-IP, T-IM, ICIP, GRSL, and OSA OL, OE. He is a member of the IEEE.
Sept. 1997-July 2001 Huazhong University of Science and Technology B.S. in Electronic and Information Engineering
Sept. 2001-Mar 2007 Huazhong University of Science and Technology Ph.D. in Pattern Recognition and Intelligent System
Mar. 2013- present: Associate Professor, School of Automation, HUST.
Nov. 2010- Mar. 2013: Associate Professor, IPRAI (Institute of Pattern Recognition and Artificial Intelligence), HUST.
Mar. 2007-Oct. 2009: Postdoctoral Researcher, School of Computer Science and Technology, HUST.
Image deconvolution and restoration
1.Yan Luxin, Wu tao, Zhong Sheng, and Zhang Qiude, A variation-based ring artifact correction method with sparse constraint for flat-detector CT, Physics In Medicine and Biology, Vol. 61, No. 3, pp. 1278-1292, 2016.
2.Chang Yi, Yan Luxin, Fang Houzhang, and LuoChunan, Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping, IEEE Trans. on Image Processing, Vol. 24, No. 6, pp. 1852-1866, 2015.
3.Chang Yi, Yan Luxin, Fang Houzhang, and Liu Hai, Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation, IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 6, pp. 1051-1055, 2014.
4.Fang Houzhang, Yan Luxin, Multiframe blind image deconvolution with split Bregman method, International Journal for Light and Electron Optics, Vol. 125,
No. 1, pp. 446-451, 2014.
5.Fang Houzhang, Yan Luxin, Parametric blind deconvolution for passive millimeter wave images with framelet regularization, International Journal for Light and Electron Optics, Vol. 125, No. 3, pp. 1454-1460, 2014.
6.Chang Yi, Fang Houzhang, Yan Luxin, and Liu Hai, Robust destriping method with unidirectional total variation and framelet regularization, Optics Express, Vol. 21, No. 20, pp. 23307-23323, 2013.
7.Fang Houzhang, Yan Luxin, Liu Hai, and Chang Yi, Blind Poissonian images deconvolution with framelet regularization, Optics Letters, Vol. 38, No. 4, pp. 389-391, 2013.
8.Fang Houzhang, Yan Luxin, Poissonian image deconvolution with analysis sparsity priors, Journal of Electronic Imaging, Vol. 22, No. 2, pp. 23033(1-10), 2013.
9.Yan Luxin, Liu Hai, Chen Liqun, Fang Houzhang, Chang Yi, and Zhang Tianxu, Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images, Journal of Modern Optics, Vol. 60, No. 12, pp. 970-982, 2013.
10.Yan Luxin, Fang Houzhang, and Zhong Sheng, Blind image deconvolution with spatially adaptive total variation regularization, Optics Letters, Vol. 37, No. 14, pp. 2778-2780, 2012.
11.Yan Luxin, JinMingzhi, Fang Houzhang, Liu Hai, and Zhang Tianxu, Atmospheric- turbulence-degraded astronomical image restoration by minimizing second-order central moment, IEEE Geoscience and Remote Sensing Letters, Vol. 9, No. 4, pp. 672-676, 2012.
12.Chang Yi, Fang Houzhang, Yan Luxin, and Liu Hai, Joint blind deblurring and destriping for remote sensing images, 20th IEEE International Conference on Image Processing (ICIP), Sept. 15-18, pp. 469-473, Melbourne, Australia, 2013, Oral Report.
Infrared Spectral signal processing
13.Liu Hai, Yan Luxin, Chang Yi, Fang Houzhang, and Zhang Tianxu, Spectral deconvolution and feature extraction with robust adaptive Tikhonov regularization, IEEE Transactions on Instrumentation and Measurement, Vol. 62, No. 2, pp. 315-327, 2013.
14.Yan Luxin, Liu Hai, Zhong Sheng, and Fang Houzhang, Semi-blind spectral deconvolution with adaptive Tikhonov regularization, Applied spectroscopy, Vol. 66, No. 11, pp. 1334-1346, 2012.
15.Liu Hai, Zhang Tianxu, Yan Luxin, Fang Houzhang, and Chang Yi, A MAP-based algorithm for spectroscopic semi-blind deconvolution, The Analyst, Vol. 137, No. 16, pp. 3862-3873, 2012.
Object detection and Recognition
16.Zheng Zhenzhu, Zhang Tianxu, and Yan Luxin, Saliency model for object detection: searching for novel items in the scene, Optics Letters, Vol. 37, No. 9, pp. 1580-1582, 2012.
17.Zhong Sheng, Wang Jianhui,and Yan Luxin, A real-time embedded architecture for SIFT, Journal of Systems Architecture, Vol. 59, No. 1, pp. 16-29, 2013.
18.Wang Jianhui,Zhong Sheng, Yan Luxin, and Cao Zhiguo, An embedded system -on-a-chip architecture for real-time visual detection and matching, IEEE Transactions on Circuits and Systems for Video Technology,Vol. 24, No. 3, pp. 525-538, 2014.
1.Excellent Advisor for Master
Design for Embedded Image Processing System
Real-time Image Processing
Current Research Projects：
1.Jan. 2016-Dec.2019: National Natural Science Foundation of China (Grant NO. 61571207):“Restoration method for ground-based images of space objects based on spatio-temporal structural self-similarity and sparse representation”.
2.Jan. 2015-Dec.2019: Key Project of National Natural Science Foundation of China (Grant NO. 61433007):“Real-time Recognition Method and Key Technology of Optical Guidance for High Speed Aircraft”.
3.Several application projects about image processing, such as SAR imaging speckle reduction, and THz images enhancement.
Finished Research Projects:
2010-2012: National Natural Science Foundation of China (Grant NO. 60902060):“Research on Key Issues of Super-Resolution for Passive Millimeter Focal Plane Array Imaging System”.
2010-2011: China Postdoctoral Science Special Foundation: “Super-Resolution Method for Passive Millimeter-Wave Imaging Basing on the Degradation Model”.