Prof. Bingqi Liu Profile

Prof. Bingqi Liu

at Army Engineering Univ

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Publications (2)

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SPIE Journal Paper | 17 June 2019

Multimodel fusion method via sparse representation at pixel-level and feature-level

Shuai Zhang, Bingqi Liu, Fuyu Huang

OE, Vol. 58, Issue 06, 063105, (June 2019) https://doi.org/10.1117/12.10.1117/1.OE.58.6.063105

KEYWORDS: Image fusion, Associative arrays, Independent component analysis, Principal component analysis, Optical engineering, Medical imaging, Visualization, Information visualization, Tolerancing, Image quality

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In current fusion methods based on sparse representation (SR) and different frequency, the SR is usually applied to fusion of the low-frequency components. In contrast, the direct fusion is usually adopted for high-frequency components due to their significant diversity. However, the effect of the direct fusion is degraded by the redundant information resulting from the correlation between original signals. A multimodel fusion framework is proposed by applying the SR to low-frequency fusion at pixel-level and high-frequency fusion at feature-level, respectively. First, the multimodal images are decomposed into high-frequency and low-frequency components by nonsubsampled contourlet transform (NSCT). Second, the universal high-frequency dictionary is constructed by using the fast independent component analysis (ICA) of the source high-frequency and its subband images. They represent the general feature part and unique feature part for the high-frequency signals, respectively. The universal low-frequency dictionary is constructed by using the original low-frequency signals. Third, the direct fusion of the high-frequency is converted into sparse coefficients fusion in fast ICA domain. Moreover, the multiple directive contrasts by modifying sum-modified Laplacian are taken as the fusion rule. The low-frequency signals are fused by using an activity measure based on weights. Finally, the fused image is obtained by inverse NSCT on the merged components. The experiments are conducted on three types of image pairs, and the results demonstrate that the proposed method outperforms seven state-of-the-art methods, in terms of four subjective and objective evaluations.

Proceedings Article | 18 January 2019 Paper

Optical axis parallelism calibration in three-channel filter-based non-imaging passive ranging system based on oxygen absorption

Hao Yu, Bingqi Liu, Shuai Zhang, Xishan Wu, Yichao Chen, Yu Zhang, Zongqun Yan, Ziang Wang

Proceedings Volume 10839, 108390Z (2019) https://doi.org/10.1117/12.2505162

KEYWORDS: Ranging, Oxygen, Calibration, Absorption, Optical calibration, Prisms, CCD cameras, Reticles

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In order to solve the problem of the poor real-time measurements caused by using hyperspectral imaging spectrometers in previous work and simplify the system structure, a three-channel filter-based non-imaging passive ranging system based on oxygen absorption is designed. In this system, there are three independent spectral measurement channels, which are corresponded to the oxygen A absorption band and its left and right non-absorption band shoulders respectively, and there is a sighting telescope for aiming at the target. Optical axis parallelism calibration between each spectral measurement channel and the aiming optical path is critical to ranging accuracy of this passive ranging system. A digital self-collimation optical axis parallelism calibration method is proposed. In this method, optical axis of the spectral measurement channel is drew out by a rhombic prism in parallel, and then reversed by a cube-corner prism into the aiming telescope. Both the focal plane of the spectral measurement channel and the cross reticle of the aiming telescope are imaged in the same CCD camera, the optical axis non-parallel error is measured by comparing the position of image center of the focal plane of the spectral measuring channel and the cross reticle of the aiming telescope. Then by adjusting the attitude of the spectral measurement channel until the center of the two images coincide, the optical axis parallelism can be calibrated. Potential errors of this calibration method are analyzed, and it is concluded that the precision is less than 2″, and the feasibility and accuracy of this method are verified by experiments.

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