In:
ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 31, No. 4 ( 2012-08-05), p. 1-13
Abstract:
We present a technique for performing high-dimensional filtering of images and videos in real time. Our approach produces high-quality results and accelerates filtering by computing the filter's response at a reduced set of sampling points, and using these for interpolation at all N input pixels. We show that for a proper choice of these sampling points, the total cost of the filtering operation is linear both in N and in the dimension d of the space in which the filter operates. As such, ours is the first high-dimensional filter with such a complexity. We present formal derivations for the equations that define our filter, as well as for an algorithm to compute the sampling points. This provides a sound theoretical justification for our method and for its properties. The resulting filter is quite flexible, being capable of producing responses that approximate either standard Gaussian, bilateral, or non-local-means filters. Such flexibility also allows us to demonstrate the first hybrid Euclidean-geodesic filter that runs in a single pass. Our filter is faster and requires less memory than previous approaches, being able to process a 10-Megapixel full-color image at 50 fps on modern GPUs. We illustrate the effectiveness of our approach by performing a variety of tasks ranging from edge-aware color filtering in 5-D, noise reduction (using up to 147 dimensions), single-pass hybrid Euclidean-geodesic filtering, and detail enhancement, among others.
Type of Medium:
Online Resource
ISSN:
0730-0301
,
1557-7368
DOI:
10.1145/2185520.2185529
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2012
detail.hit.zdb_id:
2006336-2
detail.hit.zdb_id:
625686-7
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