Mr. Pingping Yun Profile

Pingping Yun

at Xi'an Institute of Applied Optics

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Proceedings Article | 23 November 2024 Paper

Region texture analysis based Laplacian pyramid color transfer ICA method

Yuan Luo, Ping Chen, Hongqiang Lu, Pingping Yun, Yingqi Liu

Proceedings Volume 13238, 132380J (2024) https://doi.org/10.1117/12.3032729

KEYWORDS: Image fusion, Color, Independent component analysis, Image enhancement, Feature fusion, Image processing, Cooccurrence matrices, Feature extraction

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Independent Component Analysis(ICA) applied in the field of image processing is a noval transformation domain method that utilizes sparse coding on the basis of analyzing the characteristics of the human visual system. It has multidirectionality, feature extraction, and edge modeling characteristics. Color transfer is currently the best way to integrate natural color in images. On the basis of effectively integrating ICA method and color transfer algorithm, a region texture color transfer-ICA natural color fusion algorithm is proposed by organically combining the matching parameters and transfer parameters of color transfer. Dynamic online method training ICA domain decomposition kernel function and synthesis kernel function; generating grayscale fusion images according to regional energy fusion rules; using grayscale image color transfer algorithm based on regional Gray Level Co-occurrence Matrix(GLCM) texture to extract texture features of reference images, achieving optimal matching with regional texture features of grayscale fusion images, and linearly assigning first-order and second-order color information to grayscale images to generate source color image; the Laplacian pyramid decomposes the color space channels of the source color image and color reference image into multiple resolutions for color transfer, enhancing the natural color fusion image’s representation of significant scene information such as local textures. Human visual perception and objective evaluation indicates that the fusion image highlights band features and enhances detailed information with natural and comfortable colors, further improving scene perception.

Proceedings Article | 27 November 2023 Poster + Paper

Color fusion method with a combination of steerable pyramid color transfer and ICA

Yuan Luo, Pingping Yun

Proceedings Volume 12766, 1276616 (2023) https://doi.org/10.1117/12.2680899

KEYWORDS: Image fusion, Color, Image enhancement, Independent component analysis, Eye, Mid infrared, Image quality

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Independent Component Analysis (ICA) applied to the image processing is the analysis of the characteristics of the human visual system based on sparse code. ICA provides a novel transfer domain method along multiple directions and offers excellent characteristic expression and an edge modeling feature. Color transfer is currently the best way to obtain natural colorization for grayscale image. By combining these two methods, our study focused on the exploration of an approach to natural color fusion by highlighting the corresponding band characteristics. A training image database was established and the characteristics of independent band were extracted to construct an analysis kernel and a synthesis kernel of the ICA domain. In the ICA domain, we applied regional energy fusion rule to generate a grayscale fusion image. According to the visual task, the source image was linearly projected to the color channels in brightness-color separation color space, giving color information to the grayscale fused image. Using a steerable pyramid, the sub-band image of source color image and the reference color image were generated and transferred with the mean and variance independently. Finally, a fused image with a daytime color appearance was obtained. The perception of the human eye and the objective evaluation indicated that the fused image highlights the band characteristics and enhances details with natural and visually pleasing colors, which further improves scene perception.

Proceedings Article | 27 March 2022 Paper

A fusion imaging system with visible light camera and photon counting lidar

Libing Hou, Zhong Tao, Qingqing Xu, Longyang Kong, Bo Hu, Pingping Yun, Jing Wang, Zhonghong Bu

Proceedings Volume 12169, 121698T (2022) https://doi.org/10.1117/12.2625185

KEYWORDS: Imaging systems, Photon counting, Image fusion, 3D image processing, LIDAR, Image processing

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Photon counting lidar is a high-sensitivity laser active imaging method. It can obtain more three-dimensional point cloud data under the same size, weight and power consumption. Through the use of photon counting lidar and image fusion process, the electro-optical system, such as visible light and infrared camera, could generate grayscale images and videos with distance, position and other information of the targets. In this paper, a Fusion Imaging System with Visible Light Camera and Photon Counting Lidar was designed. Visible light image and high-resolution photon counting threedimensional point clouds could be obtained by the system. Fiber array coupled Geiger-APDs were used as the single photon detectors in the system to acquire three-dimensional information with two-dimensional scanner. A CMOS camera was used to acquire gray visible light image in the system. The time-correlated single photon counting (TCSPC) filter algorithm was used to process the single photon points in order to filter the noise signals and extract valid signals. The fusion processing algorithm of the imaging system was designed by using the direct linear transformation algorithm. The performance of the system was verified through experiments. The results show that the three-dimensional imaging range exceeds 1000m under the day light condition. The ranging accuracy of the system is 0.083m. Pixel-level fusion of visible light image and three-dimensional image could be realized at 1024×768 resolution, which effectively improves the detection and recognition capabilities of the system.

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