基于运行和的高效高精度高斯图像滤波
Efficient and Accurate Gaussian Image Filtering Using Running Sums
摘要：
提出了一种简单有效的高斯核图像卷积方法。该计算是使用沿图像行和列的运行和在每个像素的恒定数量的操作中执行的。我们调查的误差函数用于核近似和它的关系的输入信号的属性。基于自然图像统计，我们提出了一个二次型核误差函数，使输出图像l2误差最小化。我们应用所提出的方法来近似高斯核的线性组合常数函数。这导致非常有效的高斯滤波方法。我们的实验表明，所提出的技术比最先进的方法更快，同时保持类似的精度。
英文摘要：
This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns. We investigate the error function used for kernel approximation and its relation to the properties of the input signal. Based on natural image statistics we propose a quadratic form kernel error function so that the output image l2 error is minimized. We apply the proposed approach to approximate the Gaussian kernel by linear combination of constant functions. This results in very efficient Gaussian filtering method. Our experiments show that the proposed technique is faster than state of the art methods while preserving a similar accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
