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Invariants based blur classification algorithm

机译:基于不变式的模糊分类算法

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Extraction of information from an image acquired by real imaging systems is a difficult task, since the observed image may be degraded by blurring. In this paper, a framework for classification of blur in an image is presented and a technique for classification of blur using invariants is proposed. In this method, the blur classification is carried out without estimating the blurring function. The proposed technique is applied on a large dataset of images degraded by motion blur, Gaussian blur and defocus blur. The simulation results show that the proposed method gives accurate classification of the blur present in an image.
机译:从实际成像系统获取的图像中提取信息是一项艰巨的任务,因为观察到的图像可能会因模糊而退化。本文提出了一种用于图像模糊分类的框架,并提出了一种使用不变量进行模糊分类的技术。在该方法中,在不估计模糊函数的情况下执行模糊分类。所提出的技术被应用于通过运动模糊,高斯模糊和散焦模糊而降级的图像的大型数据集。仿真结果表明,所提出的方法可以对图像中存在的模糊进行准确分类。

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