Prof. Zhengzhou Wang Profile

Prof. Zhengzhou Wang

at Xi'an Jiaotong Univ

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Publications (9)

Proceedings Article | 18 December 2023 Paper

The detection method of sidelobe peaks parameter in weak signal regions based on NVPCA image enhancement

Zhengzhou Wang, Jitong Wei, Li Wang

Proceedings Volume 12963, 129631S (2023) https://doi.org/10.1117/12.3009387

KEYWORDS: Image processing, Image segmentation, Background noise, Signal processing, Samarium

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Proceedings Article | 5 November 2020 Paper

The method for calculating optical target center of weak contrast collimating image in integrated diagnosis system

Zhengzhou Wang, Li Wang, Meng Tan, Yongquan Chen, Gang Li, Dongchi Yi, Jitong Wei

Proceedings Volume 11567, 115670N (2020) https://doi.org/10.1117/12.2575512

KEYWORDS: Optical spheres, Image enhancement, Image processing, Integrated optics, System integration, Image classification, Binary data, Signal to noise ratio, Data centers, Principal component analysis

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In the process of fast automatic alignment of optical path in the integrated diagnosis system of host laser facility, there are six kinds of different optical target images captured. Because of the different characteristics of optical target, different methods for calculating the center of optical target image must be put forward, which increases the algorithm complexity of calculating the optical target center. In order to improve the efficiency and accuracy of fast automatic alignment of laser beam in the integrated diagnosis system, it is necessary and important to propose a method that can calculate all kinds of optical center of collimating images. In this paper, based on the synthesis of the characteristics of pinhole image, schlieren sphere image and common optical target image, a general method for calculating the optical target center of weak contrast collimating image is proposed. Firstly, multi-dimensional image cubes are constructed by using multi time-sharing images or neighborhood vectors, and the dimension of multi-dimensional data cubes is reduced by using NVPCA transformation to remove the correlation between the various dimensional images, separate and readjust the noise of original image; Secondly, the one-dimensional image data is classified by Kmeans, and the binary image is processed by the mathematical morphology operation to separate the laser target and background respectively; Thirdly, if the optical target image is a schlieren image, it need to obtain the background of schlieren image, the region of schlieren sphere, the edge of schlieren sphere respectively; If the optical target image is a pinhole image, it need to obtain the characteristic points on the circle contour of the pinhole image; Finally, the least square method is used to calculate the circle center of the pinhole image and the schlieren sphere image, and the center of gravity method is used to calculate the center of optical target. The experimental results show that this paper synthesizes the same and different characteristics of all kinds of collimating images, proposes a method to calculate the optical target center of weak contrast collimating images, improves the calculation accuracy of the optical target center of collimating images (less than 1 pixel) effectively, shortens the fast alignment time of the optical path of the integrated diagnosis system, and provides an effective guarantee for the measurement experiment of far-field focal spot of high power laser based on schlieren method.

Proceedings Article | 5 November 2020 Paper

A denoising method based on CNN for the measurement of far-field focal spot of high power laser using Schlieren method

Gang Li, Zhengzhou Wang, Li Wang, Meng Tan, Wei Wang, Dongchi Yi, Jitong Wei

Proceedings Volume 11567, 115671I (2020) https://doi.org/10.1117/12.2579390

KEYWORDS: Denoising, Charge-coupled devices, Beam splitters, Interference (communication), Signal attenuation, Image processing, Convolution, High dynamic range imaging, Reconstruction algorithms

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Proceedings Article | 31 January 2020 Paper

The method for calculating weak contrast Schlieren sphere center in integrated diagnostic fast automatic collimation system

Zhengzhou Wang, Meng Tan, Li Wang, Qing Wang, Jitong Wei, Gang Li, Dongchi Yi

Proceedings Volume 11427, 1142731 (2020) https://doi.org/10.1117/12.2552889

KEYWORDS: Optical spheres, Image processing, Collimation, Diagnostics, Digital image processing, Image enhancement, Corrosion, System integration, Integrated optics, Target detection

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Proceedings Article | 18 December 2019 Paper

Target detection method for small defects in ink area of planar glass element

Wenbo Qi, Zhengzhou Wang, Li Wang, Meng Tan, Jitong Wei

Proceedings Volume 11342, 1134207 (2019) https://doi.org/10.1117/12.2542893

KEYWORDS: Target detection, Glasses, Defect detection, Image processing, Cell phones, Digital image processing, Binary data, Edge detection, Image segmentation, Data processing

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In the detection of small and weak defect targets in ink area of planar glass element of a mobile phone，using linear array camera with dark field illumination and line-by-line scanning imaging system will result in the size of image (30720 *16384) much larger than the size of small defect targets (3 pixels) . At the same time, because the stains are located in the ink area, the contrast between the defect target and the background of the ink area could be very low. This will lead to the weak defect targets in the ink area could not be detected quickly and effectively using the common target detection method. In order to solve this problem, a detection method of small and weak defects in the ink area of planar glass element based on self-correlation template matching and one-dimensional maximum entropy is proposed in this paper. Firstly, the large-scale image is first clipped, and the character information in the ink area is recognized and fixed position using the self-correlation template matching algorithm. The character and Logo information in the ink area are clipped according to the positioning results. Secondly, the processed image is clipped twice and binarized by the OTSU method. BLOB technology is used to select the largest white area as the ink area in the second clipping image. Thirdly, Sobel operator is used to detect the edge of the ink area, and the transitional area with the width of 100 pixels on the edge of the ink area is clipped, so the clipping image of the ink area which only contained valid small and weak defect targets is obtained. Finally, One-dimensional maximum entropy algorithm is used to separate the defect targets from real ink area, and the weak and small defect targets are recognized and detected by BLOB technology. The experimental results show that the method solves the problem of detecting the small and weak defects in the ink area of the planar glass element with fast recognition speed and high detection accuracy. It can be applied in the process of detecting the quality and cleanliness of planar glass element, and has great significance for improving the quality and efficiency of mobile phone production and assembly.

Proceedings Article | 12 December 2018 Paper

A fast method for searching the best matching point of the automatic reconstruction of focal spot measurement using the steepest descent method

Zhengzhou Wang, Yanchao Wang, Gang Li, Meng Tan, Wei Wang

Proceedings Volume 10846, 108460U (2018) https://doi.org/10.1117/12.2503966

KEYWORDS: Image processing, High dynamic range imaging, Image fusion, System integration, 3D image reconstruction, Laser cutting, Diagnostics, Charge-coupled devices, High power lasers, Laser systems engineering

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The schlieren method was the most common method for measuring the far-field focal spot in high power laser facility. The reconstruction of far-field focal spot was used by manual and autocorrelation matching method usually. Although the problem of the automatic reconstruction of focal spot was solved by the autocorrelation matching method, it searched the best matching point in the whole region of the mainlobe cutting image, which would greatly reduce the efficiency of the reconstruction of far-field focal spot. To solve this problem, a fast method for searching the best matching point based on steepest descent method was proposed in this paper. Firstly, the mainlobe image and sidelobe image were cut into the mainlobe cutting image and sidelobe cutting image, which were named as cutzb and cutpb respectively, and the size of cutting images was 300*300.Then a new image that a circle the same size as the schlieren ball was dug from cutzb image was named as cutzb', while cutzb' and cutpb were converted into two column vectors respectively, and the SAM values of the two vectors are calculated. Secondly, calculated the gradients of 8 neighborhoods of current pixel, the next searching position was the neighborhood located in the direction of the largest gradient (the largest decrease) of 8 neighborhoods while using the steepest descent method. After all the SAM values of the two vectors that met requirement were searched. The upper left corner of mainlobe cutting image was the best matching point when the SAM value was minimal. Finally, according to the best matching point, the far-field focal spot was merged by the mainlobe image and sidelobe image, and the weighted average method was applied to fuse the stitching area of the merged image. The experimental results show that the method can not only realize the automatic reconstruction of the measurement of far-field focal spot while using the schlieren method, but also greatly improve the efficiency of the reconstruction of the far-field focal spot, and realize the accurate measurement of the far-field focal spot in the high dynamic range.

Proceedings Article | 29 August 2016 Paper

Surface ship target detection in hyperspectral images based on improved variance minimum algorithm

Zhengzhou Wang, Qinye Yin, Hongguang Li, Bingliang Hu

Proceedings Volume 10033, 100330R (2016) https://doi.org/10.1117/12.2243872

KEYWORDS: Target detection, Hyperspectral imaging, Hyperspectral target detection, Detection and tracking algorithms, Mathematical morphology, Signal detection, Image processing, Image classification, Binary data, Data processing

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Proceedings Article | 8 October 2015 Paper

The research on measurement technology of high dynamic range laser focal spot

Zhengzhou Wang, Bingliang Hu, Qinye Yin, Shikang Cao, Wei Wang

Proceedings Volume 9677, 96772T (2015) https://doi.org/10.1117/12.2197290

KEYWORDS: Image fusion, Reconstruction algorithms, Calibration, Charge-coupled devices, 3D image reconstruction, High dynamic range imaging, Mathematical modeling, Diagnostics, System integration, Automatic alignment

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Proceedings Article | 19 July 2013 Paper

Algorithm of semicircular laser spot detection based on circle fitting

Zhengzhou Wang, Ruihua Xu, Bingliang Hu

Proceedings Volume 8878, 88782Z (2013) https://doi.org/10.1117/12.2030791

KEYWORDS: Detection and tracking algorithms, Image segmentation, Edge roughness, Image processing, Automatic alignment, Laser damage threshold, Laser systems engineering, Image processing algorithms and systems, Laser processing, Edge detection

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