Real-time O(1) bilateral filtering
Real-time O(1) bilateral filtering
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DOI：
10.1109/cvpr.2009.5206542
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Qingxiong Yang;K. Tan;N. Ahuja
中科院分区：
文献类型：
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作者：
Qingxiong Yang;K. Tan;N. Ahuja
我们提出了一种新的双边滤波算法，其计算复杂度不随滤波核大小而变化，即O（1）或常数时间。通过证明双边滤波器可以分解为多个恒定时间空间滤波器，我们的方法产生了一类新的恒定时间双边滤波器，可以具有任意空间和任意范围的内核。相比之下，目前可用的恒定时间算法需要使用特定的空间或特定的范围内核。此外，我们的算法适合于并行实现，导致我们所知道的第一个实时O（1）算法。同时，我们的算法产生更高质量的结果，因为我们是有效地量化的范围函数，而不是量化的范围函数和输入图像。实验结果表明，该算法不仅具有较高的PSNR，而且速度比现有算法快10倍左右，而且内存占用量小，仅为现有算法的2%。我们还表明，我们的算法可以很容易地扩展为O（1）中值滤波。我们的双边滤波算法在许多应用中进行了测试，包括高清视频会议、视频摘要、高光去除和多焦点成像。
We propose a new bilateral filtering algorithm with computational complexity invariant to filter kernel size, so-called O(1) or constant time in the literature. By showing that a bilateral filter can be decomposed into a number of constant time spatial filters, our method yields a new class of constant time bilateral filters that can have arbitrary spatial and arbitrary range kernels. In contrast, the current available constant time algorithm requires the use of specific spatial or specific range kernels. Also, our algorithm lends itself to a parallel implementation leading to the first real-time O(1) algorithm that we know of. Meanwhile, our algorithm yields higher quality results since we are effectively quantizing the range function instead of quantizing both the range function and the input image. Empirical experiments show that our algorithm not only gives higher PSNR, but is about 10× faster than the state-of-the-art. It also has a small memory footprint, needed only 2% of the memory required by the state-of-the-art for obtaining the same quality as exact using 8-bit images. We also show that our algorithm can be easily extended for O(1) median filtering. Our bilateral filtering algorithm was tested in a number of applications, including HD video conferencing, video abstraction, highlight removal, and multi-focus imaging.