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System and method for classifying the blur state of digital image pixels

机译:用于对数字图像像素的模糊状态进行分类的系统和方法

摘要

A blur classification module may compute the probability that a given pixel in a digital image was blurred using a given two-dimensional blur kernel, and may store the computed probability in a blur classification probability matrix that stores probability values for all combinations of image pixels and the blur kernels in a set of likely blur kernels. Computing these probabilities may include computing a frequency power spectrum for windows into the digital image and/or for the likely blur kernels. The blur classification module may generate a coherent mapping between pixels of the digital image and respective blur states, or may perform a segmentation of the image into blurry and sharp regions, dependent on values stored in the matrix. Input image data may be pre-processed. Blur classification results may be employed in image editing operations to automatically target image subjects or background regions, or to estimate the depth of image elements.
机译:模糊分类模块可以使用给定的二维模糊核计算数字图像中的给定像素被模糊的概率,并且可以将计算的概率存储在模糊分类概率矩阵中,该矩阵存储图像像素和像素的所有组合的概率值。一组可能的模糊内核中的模糊内核。计算这些概率可以包括计算进入数字图像的窗口和/或可能的模糊核的功率频谱。模糊分类模块可以根据存储在矩阵中的值,在数字图像的像素与各个模糊状态之间生成相干映射,或者可以将图像分割为模糊区域和锐利区域。输入图像数据可以被预处理。模糊分类结果可用于图像编辑操作中,以自动将图像对象或背景区域作为目标,或估计图像元素的深度。

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