In this paper, we focus on removing interference of motion blur by the derivation of motion blur invariants.Unlike earlier work, we don't restore any blurred image. Based on geometric moment and mathematical model of motion blur, we prove that geometric moments of blurred image and original image are linearly related. Depending on this property, we can analyse whether an existing moment-based feature is invariant to motion blur. Surprisingly, we find some geometric moment invariants are invariants to not only spatial transform but also motion blur. Meanwhile, we test invariance and robustness of these invariants using synthetic and real blur image datasets. And the results show these invariants outperform some widely used blur moment invariants and non-moment image features in image retrieval, classification and template matching.

翻译：在本文中, 我们专注于消除运动的干扰, 由运动模糊的变异因素产生。 与先前的工作不同, 我们不恢复任何模糊的图像。 根据几何时段和运动模糊的数学模型, 我们证明, 模糊的图像和原始图像的几何时段是线性关联的。 根据此属性, 我们可以分析一个基于瞬时的特征是否无法移动。 令人惊讶的是, 我们发现一些几何时段的变异因素不仅是空间变异, 也是运动模糊的。 同时, 我们用合成的和真实的模糊的图像数据集测试这些变异体的变异性和坚固性。 结果显示, 这些变异体在图像检索、 分类和模板匹配中, 一些被广泛使用的模糊的变异和不移动的图像特征。