Abstract
This paper presents an approximate Gaussian filter which can run in one-pass with high accuracy based on spectrum sparsity. This method is a modification of the cosine integral image (CII), which decomposes a filter kernel into few cosine terms and convolves each cosine term with an input image in constant time per pixel by using integral images and look-up tables. However, they require much workspace and high access cost. The proposed method solves the problem with no decline in quality by sequentially updating sums instead of integral images and by improving look-up tables, which accomplishes a one-pass approximation with much less workspace. A specialization for tiny kernels are also discussed for faster calculation. Experiments on image filtering show that the proposed method can run nearly two times faster than CII and also than convolution even with small kernel.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Image Processing, ICIP |
| Pages | 125-128 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL Duration: 2012 Sep 30 → 2012 Oct 3 |
Other
| Other | 2012 19th IEEE International Conference on Image Processing, ICIP 2012 |
|---|---|
| City | Lake Buena Vista, FL |
| Period | 12/9/30 → 12/10/3 |
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Keywords
- digital signal processing
- discrete cosine transform
- Gaussian filter
- sparse spectrum
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
Cite this
Fast image filtering by DCT-based kernel decomposition and sequential sum update. / Sugimoto, Kenjiro; Kamata, Seiichiro.
Proceedings - International Conference on Image Processing, ICIP. 2012. p. 125-128 6466811.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fast image filtering by DCT-based kernel decomposition and sequential sum update
AU - Sugimoto, Kenjiro
AU - Kamata, Seiichiro
PY - 2012
Y1 - 2012
N2 - This paper presents an approximate Gaussian filter which can run in one-pass with high accuracy based on spectrum sparsity. This method is a modification of the cosine integral image (CII), which decomposes a filter kernel into few cosine terms and convolves each cosine term with an input image in constant time per pixel by using integral images and look-up tables. However, they require much workspace and high access cost. The proposed method solves the problem with no decline in quality by sequentially updating sums instead of integral images and by improving look-up tables, which accomplishes a one-pass approximation with much less workspace. A specialization for tiny kernels are also discussed for faster calculation. Experiments on image filtering show that the proposed method can run nearly two times faster than CII and also than convolution even with small kernel.
AB - This paper presents an approximate Gaussian filter which can run in one-pass with high accuracy based on spectrum sparsity. This method is a modification of the cosine integral image (CII), which decomposes a filter kernel into few cosine terms and convolves each cosine term with an input image in constant time per pixel by using integral images and look-up tables. However, they require much workspace and high access cost. The proposed method solves the problem with no decline in quality by sequentially updating sums instead of integral images and by improving look-up tables, which accomplishes a one-pass approximation with much less workspace. A specialization for tiny kernels are also discussed for faster calculation. Experiments on image filtering show that the proposed method can run nearly two times faster than CII and also than convolution even with small kernel.
KW - digital signal processing
KW - discrete cosine transform
KW - Gaussian filter
KW - sparse spectrum
UR - http://www.scopus.com/inward/record.url?scp=84875826067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875826067&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6466811
DO - 10.1109/ICIP.2012.6466811
M3 - Conference contribution
SN - 9781467325332
SP - 125
EP - 128
BT - Proceedings - International Conference on Image Processing, ICIP
ER -