基于不变量的模糊分类算法

Ruchi Gajjar, T. Zaveri, Ami J. Shukla
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引用次数: 2

摘要

从真实成像系统获取的图像中提取信息是一项困难的任务,因为观察到的图像可能会因模糊而退化。本文提出了一种图像模糊分类的框架,并提出了一种利用不变量对图像模糊进行分类的方法。该方法在不估计模糊函数的情况下进行模糊分类。将该方法应用于运动模糊、高斯模糊和散焦模糊退化的大型图像数据集。仿真结果表明,该方法对图像中存在的模糊进行了准确的分类。
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Invariants based blur classification algorithm
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.
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