Introduction to signal processing
Introduction to signal processing
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast median and bilateral filtering
ACM SIGGRAPH 2006 Papers
Real-time edge-aware image processing with the bilateral grid
ACM SIGGRAPH 2007 papers
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
International Journal of Computer Vision
Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
Edge-avoiding wavelets and their applications
ACM SIGGRAPH 2009 papers
Bilateral Filtering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Domain transform for edge-aware image and video processing
ACM SIGGRAPH 2011 papers
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast stereo matching using adaptive guided filtering
Image and Vision Computing
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This paper proposes a recursive implementation of the bilateral filter. Unlike previous methods, this implementation yields an bilateral filter whose computational complexity is linear in both input size and dimensionality. The proposed implementation demonstrates that the bilateral filter can be as efficient as the recent edge-preserving filtering methods, especially for high-dimensional images. Let the number of pixels contained in the image be N, and the number of channels be D, the computational complexity of the proposed implementation will be O(ND). It is more efficient than the state-of-the-art bilateral filtering methods that have a computational complexity of O(ND2) [1] (linear in the image size but polynomial in dimensionality) or O(Nlog(N)D) [2] (linear in the dimensionality thus faster than [1] for high-dimensional filtering). Specifically, the proposed implementation takes about 43 ms to process a one megapixel color image (and about 14 ms to process a 1 megapixel grayscale image) which is about 18 × faster than [1] and 86× faster than [2]. The experiments were conducted on a MacBook Air laptop computer with a 1.8 GHz Intel Core i7 CPU and 4 GB memory. The memory complexity of the proposed implementation is also low: as few as the image memory will be required (memory for the images before and after filtering is excluded). This paper also derives a new filter named gradient domain bilateral filter from the proposed recursive implementation. Unlike the bilateral filter, it performs bilateral filtering on the gradient domain. It can be used for edge-preserving filtering but avoids sharp edges that are observed to cause visible artifacts in some computer graphics tasks. The proposed implementations were proved to be effective for a number of computer vision and computer graphics applications, including stylization, tone mapping, detail enhancement and stereo matching.