TR2008-030
Constant Time O(1) Bilateral Filtering
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- "Constant Time O(1) Bilateral Filtering", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2008. ,
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Research Area:
Abstract:
This paper presents three novel methods that enable bilateral filtering in constant time O(1) without sampling. Constant time means that the computation time of the filtering remains same even if the filter size becomes very large. Our first method takes advantage of the integral histograms to avoid the redundant operations for bilateral filters with box spatial and arbitrary range kernels. For bilateral filters constructed by polynomial range and arbitrary spatial filters, our second method provides a direct formulation by using linear filters of image powers without any approximations. Lastly, we show that Gaussian range and arbitrary spatial bilateral filters can be expressed by Taylor series as linear filter decompositions without any noticeable degradation of filter response. All these methods drastically decrease the computational time by cutting it down constant times (e.g. to 0.06 seconds per 1MB image) while achieving very high PSNR's over 45dB. In addition to the computational advantages, our methods are straightforward to implement.
Related News & Events
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NEWS CVPR 2008: 8 publications by Jay Thornton, Shantanu D. Rane, Oncel Tuzel, Matthew Brand, Anthony Vetro and Amit K. Agrawal Date: June 28, 2008
Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contacts: Matthew Brand; Anthony VetroBrief- The papers "Non-Refractive Modulators for Encoding and Capturing Scene Appearance and Depth" by Veeraraghavan, A., Agrawal, A., Raskar, R., Mohan, A. and Tumblin, J., "Feature Transformation of Biometric Templates for Secure Biometric Systems based on Error Correcting Codes" by Sutcu, Y., Rane, S., Yedidia, J.S., Draper, S.C. and Vetro, A., "Constant Time O(1) Bilateral Filtering" by Porikli, F., "Learning on Lie Groups for Invariant Detection and Tracking" by Tuzel, O., Porikli, F. and Meer, P., "Kernel Integral Images: A Framework for Fast non-Uniform Filtering" by Hussein, M., Porikli, F. and Davis, L., "A Conditional Random Field for Automatic Photo Editing" by Brand, M. and Pletscher, P., "Boosting Adaptive Linear Weak Classifiers for Online Learning and Tracking" by Parag, T., Porikli, F. and Elgammai, A. and "Sensing Increased Image Resolution Using Aperture Masks" by Mohan, A., Huang, X., Tumblin, J. and Raskar, R. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).