Acceleration of the Shiftable $\mbi{O}{(1)}$ Algorithm for Bilateral Filtering and Nonlocal Means

A direct implementation of the bilateral filter requires <i>O</i>(σ<i>s</i><sup>2</sup>) operations per pixel, where σ<i>s</i> is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter that required <i>O</i>(1) operations per pixel with respect to σ<i>s</i> was recently proposed. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated that this idea could be extended to a larger class of filters, including the popular non-local means filter. As already observed, a flip side of this approach was that the run time depended on the width σ<i>r</i> of the range kernel. For an image with dynamic range [0,<i>T</i>], the run time scaled as <i>O</i>(<i>T</i><sup>2</sup>/σ<sub>r</sub><sup>2</sup>) with σ<i>r</i>. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. In this paper, we discuss this problem, and propose some simple steps to accelerate the implementation, in general, and for small σ<i>r</i> in particular. We provide some experimental results to demonstrate the acceleration that is achieved using these modifications.

[1]  Andrew Adams,et al.  Fast High‐Dimensional Filtering Using the Permutohedral Lattice , 2010, Comput. Graph. Forum.

[2]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[3]  Ben Weiss Fast median and bilateral filtering , 2006, SIGGRAPH 2006.

[4]  Fatih Porikli,et al.  Constant time O(1) bilateral filtering , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Narendra Ahuja,et al.  Real-time O(1) bilateral filtering , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Michael Unser,et al.  Fast Space-Variant Elliptical Filtering Using Box Splines , 2010, IEEE Transactions on Image Processing.

[8]  Bahadir K. Gunturk,et al.  Fast bilateral filter with arbitrary range and domain kernels , 2010, 2010 IEEE International Conference on Image Processing.

[9]  L. P. I︠A︡roslavskiĭ Digital picture processing : an introduction , 1985 .

[10]  Frédo Durand,et al.  A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach , 2006, International Journal of Computer Vision.

[11]  Michael Unser,et al.  Fast $O(1)$ Bilateral Filtering Using Trigonometric Range Kernels , 2011, IEEE Transactions on Image Processing.

[12]  Hui Cheng,et al.  Bilateral Filtering-Based Optical Flow Estimation with Occlusion Detection , 2006, ECCV.

[13]  Noga Alon,et al.  The Probabilistic Method , 2015, Fundamentals of Ramsey Theory.

[14]  C CrowFranklin Summed-area tables for texture mapping , 1984 .

[15]  Paul S. Heckbert,et al.  Filtering by repeated integration , 1986, SIGGRAPH.

[16]  Noga Alon,et al.  The Probabilistic Method, Second Edition , 2004 .

[17]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[18]  Wesley E. Snyder,et al.  Adaptive demosaicking , 2003, J. Electronic Imaging.

[19]  D. Nistér,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Chandra Sekhar Seelamantula,et al.  Optimal parameter selection for bilateral filters using Poisson Unbiased Risk Estimate , 2012, 2012 19th IEEE International Conference on Image Processing.

[21]  Leonard McMillan,et al.  Multispectral Bilateral Video Fusion , 2007, IEEE Transactions on Image Processing.

[22]  Marcel van Herk A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels , 1992, Pattern Recognit. Lett..

[23]  Franklin C. Crow,et al.  Summed-area tables for texture mapping , 1984, SIGGRAPH.

[24]  Kunal Narayan Chaudhury,et al.  Constant-Time Filtering Using Shiftable Kernels , 2011, IEEE Signal Processing Letters.

[25]  Holger Winnemöller,et al.  Real-time video abstraction , 2006, ACM Trans. Graph..

[26]  Michael Werman,et al.  Computing 2-D Min, Median, and Max Filters , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Chandra Sekhar Seelamantula,et al.  Sure-fast bilateral filters , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[28]  Guillermo Sapiro,et al.  Fast image and video denoising via nonlocal means of similar neighborhoods , 2005, IEEE Signal Processing Letters.