Dr. Zhi-Guo Wu Profile

Dr. Zhi-Guo Wu

at CIOMP

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Publications (4)

Proceedings Article | 24 November 2014 Paper

The real-time complex cruise scene motion detection system based on DSP

Zhi-guo Wu, Ming-jia Wang

Proceedings Volume 9301, 93010Y (2014) https://doi.org/10.1117/12.2070700

KEYWORDS: Digital signal processing, Image processing, Target detection, Field programmable gate arrays, Motion detection, Target recognition, Digital image processing, Control systems, Video, Embedded systems

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Dynamic target recognition is an important issue in the field of image processing research. It is widely used in photoelectric detection, target tracking, video surveillance areas. Complex cruise scene of target detection, compared to the static background, since the target and background objects together and both are in motion, greatly increases the complexity of moving target detection and recognition. Based on the practical engineering applications, combining an embedded systems and real-time image detection technology, this paper proposes a real-time movement detection method on an embedded system based on the FPGA + DSP system architecture on an embedded system. The DSP digital image processing system takes high speed digital signal processor DSP TMS320C6416T as the main computing components. And we take large capacity FPGA as coprocessor. It is designed and developed a high-performance image processing card. The FPGA is responsible for the data receiving and dispatching, DSP is responsible for data processing. The FPGA collects image data and controls SDRAM according to the digital image sequence. The SDRAM realizes multiport image buffer. DSP reads real-time image through SDRAM and performs scene motion detection algorithm. Then we implement the data reception and data processing parallelization. This system designs and realizes complex cruise scene motion detection for engineering application. The image edge information has the anti-light change and the strong anti-interference ability. First of all, the adjacent frame and current frame image are processed by convolution operation, extract the edge images. Then we compute correlation strength and the value of movement offset. We can complete scene motion parameters estimation by the result, in order to achieve real-time accurate motion detection. We use images in resolution of 768 * 576 and 25Hz frame rate to do the real-time cruise experiment. The results show that the proposed system achieves real-time processing requirements for the engineering applications.

Proceedings Article | 11 September 2013 Paper

The technology of forest fire detection based on infrared image

Zhi-guo Wu, Guo-juan Liu, Ming-jia Wang, Suo-jian Wang

Proceedings Volume 8907, 89072C (2013) https://doi.org/10.1117/12.2032967

KEYWORDS: Image segmentation, Infrared imaging, Flame detectors, Infrared radiation, Infrared detectors, Image processing, Image registration, Target detection, Infrared technology, Detection and tracking algorithms

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Proceedings Article | 21 August 2013 Paper

An adaptive algorithm for low contrast infrared image enhancement

Sheng-dong Liu, Cheng-yuan Peng, Ming-jia Wang, Zhi-guo Wu, Jia-qi Liu

Proceedings Volume 8908, 890810 (2013) https://doi.org/10.1117/12.2033010

KEYWORDS: Image enhancement, Detection and tracking algorithms, Infrared imaging, Infrared radiation, Visualization, Algorithms, Image compression, Image processing, Digital imaging, Image contrast enhancement

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An adaptive infrared image enhancement algorithm for low contrast is proposed in this paper, to deal with the problem that conventional image enhancement algorithm is not able to effective identify the interesting region when dynamic range is large in image. This algorithm begin with the human visual perception characteristics, take account of the global adaptive image enhancement and local feature boost, not only the contrast of image is raised, but also the texture of picture is more distinct. Firstly, the global image dynamic range is adjusted from the overall, the dynamic range of original image and display grayscale form corresponding relationship, the gray scale of bright object is raised and the the gray scale of dark target is reduced at the same time, to improve the overall image contrast. Secondly, the corresponding filtering algorithm is used on the current point and its neighborhood pixels to extract image texture information, to adjust the brightness of the current point in order to enhance the local contrast of the image. The algorithm overcomes the default that the outline is easy to vague in traditional edge detection algorithm, and ensure the distinctness of texture detail in image enhancement. Lastly, we normalize the global luminance adjustment image and the local brightness adjustment image, to ensure a smooth transition of image details. A lot of experiments is made to compare the algorithm proposed in this paper with other convention image enhancement algorithm, and two groups of vague IR image are taken in experiment. Experiments show that: the contrast ratio of the picture is boosted after handled by histogram equalization algorithm, but the detail of the picture is not clear, the detail of the picture can be distinguished after handled by the Retinex algorithm. The image after deal with by self-adaptive enhancement algorithm proposed in this paper becomes clear in details, and the image contrast is markedly improved in compared with Retinex algorithm.

Proceedings Article | 18 August 2011 Paper

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

Zhi-guo Wu, Ming-jia Wang, Guang-liang Han

Proceedings Volume 8194, 819410 (2011) https://doi.org/10.1117/12.899653

KEYWORDS: Image fusion, Wavelets, Neurons, Wavelet transforms, Image processing, Evolutionary algorithms, Detection and tracking algorithms, Brain mapping, Pulse generators, Neural networks

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Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover, comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image.

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