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Research on Infrared Image Characteristics and Applications Based on Generalized Wavelet Transform

Author: BaGuiJie
Tutor: ZhangZuo
School: Harbin Institute of Technology
Course: Information and Communication Engineering
Keywords: Infrared image characteristics Curvelet transform Image Denoising Edge Detection Background suppression
CLC: TP391.41
Type: Master's thesis
Year: 2007
Downloads: 89
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Abstract


In recent years, the infrared image is widely used in military, civilian, however, due to the inherent characteristics of the infrared imaging system, edge blur, noise pollution and other phenomena have a serious impact on the quality of the infrared image, in order to improve the quality, ease of application, this paper introduces a generalized wavelet transform technology. Generalized wavelet transform to represent the high-dimensional space in a straight line or curve singularity signal available sparse coefficients, which broadens the scope of application of the wavelet analysis, and is widely used in many areas of image processing. The Curvelet transform combination of the Ridgelet transform the anisotropic characteristics and characteristics of the multi-scale wavelet transform, completeness edge as the basic representation element. In view of this, the paper start from the analysis of the characteristics of the infrared image based on Curvelet transform the system to study the infrared image denoising, edge detection, background clutter suppression technology. The first system to study mechanisms and characteristics of infrared imaging based on analysis of the statistical properties of the infrared image histogram further characteristics of the noise generated by the infrared imaging system, the infrared image of the target as well as the background statistical properties are analyzed in detail , provides a theoretical basis for technical applications. Secondly, based on the theoretical basis of the generalized wavelet transform, to study and to achieve them the Curvelet transform basic algorithm and its applications in image processing. The Curvelet transform embodied superiority, more suitable for research applications. Finally, Curvelet transform technology is applied to the three aspects of the infrared image processing. Approximate Gaussian noise process of infrared imaging detector formed of one kind of the Curvelet Transform hardness threshold compromise denoising method, the method to improve the visual effect of the image; addition, for edge detection of the infrared image, combined Curvelet transform operator infrared image edge detection, detection of sharp edges, good continuity and Roberts; Third, for weak target detection in infrared image contains the small target in infrared image background clutter suppression, the weak and small target image enhancement. By computer simulation of the above algorithm, the results are better than traditional algorithms to achieve the intended purpose of the experiment. In addition, a brief introduction from the two aspects of subjective and objective image quality evaluation standard, provides a strong basis for image quality evaluation after treatment.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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