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The simulation of adaptive optical image even and pulse noise and research of image quality evaluation

机译：自适应光学图像偶像和脉冲噪声的仿真及图像质量评价研究

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As optical image becomes more and more important in adaptive optics area, and adaptive optical telescopes play a more and more important role in the detection system on the ground, and the images we get are so many that we need find a suitable method to choose good quality images automatically in order to save human power, people pay more and more attention in image's evaluation methods and their characteristics. According to different image degradation model, the applicability of different image's quality evaluation method will be different. Researchers have paid most attention in how to improve or build new method to evaluate degraded images. Now we should change our way to take some research in the models of degradation of images, the reasons of image degradation, and the relations among different degraded images and different image quality evaluation methods. In this paper, we build models of even noise and pulse noise based on their definition and get degraded images using these models, and we take research in six kinds of usual image quality evaluation methods such as square error method, sum of multi-power of grey scale method, entropy method, Fisher function method, Sobel method, and sum of grads method, and we make computer software for these methods to use easily to evaluate all kinds of images input. Then we evaluate the images' qualities with different evaluation methods and analyze the results of six kinds of methods, and finally we get many important results. Such as the characteristics of every method for evaluating qualities of degraded images of even noise, the characteristics of every method for evaluating qualities of degraded images of pulse noise, and the best method to evaluate images which affected by two kinds of noise both and the characteristics of this method. These results are important to image's choosing automatically, and this will help us to manage the images we get through adaptive optical telescopes base on the ground.
机译：由于光学图像在自适应光学区域变得越来越重要，并且自适应光学望远镜在地面的检测系统中发挥越来越重要的作用，我们得到的图像是我们需要找到合适的方法选择良好的图像质量图像自动以节省人力，人们在图像的评估方法及其特征中支付越来越多的关注。根据不同的图像劣化模型，不同图像质量评估方法的适用性将不同。研究人员在如何改进或建立新方法中得到了最多的关注来评估劣化的图像。现在，我们应该改变我们在图像劣化模型中进行一些研究，图像劣化的原因以及不同降级图像的关系和不同的图像质量评估方法。在本文中，我们基于定义构建甚至噪声和脉冲噪声的模型，并使用这些模型获得劣化的图像，我们采用了六种通常的图像质量评估方法，如方误差方法，多功能总和灰度法，熵方法，Fisher功能方法，Sobel方法和毕业方法和级别，我们为这些方法制作了计算机软件，可以轻松地评估各种图像输入。然后，我们评估了不同评估方法的图像的品质，并分析了六种方法的结果，最后我们得到了许多重要结果。例如用于评估偶数噪声的降级图像质量的每个方法的特征，用于评估脉冲噪声的降级图像质量的每个方法的特征，以及评估受两种噪声影响的图像的最佳方法和特征这个方法。这些结果对于图像自动选择很重要，这将有助于我们管理我们通过地面上通过自适应光学望远镜的图像进行管理。

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