A Gentle Introduction to Bilateral Filtering and its Applications
“Fixing the Gaussian Blur”: the Bilateral Filter
Sylvain Paris – MIT CSAIL
Fixing the Gaussian Blur: the Bilateral Filter Sylvain Paris MIT - - PowerPoint PPT Presentation
A Gentle Introduction to Bilateral Filtering and its Applications Fixing the Gaussian Blur: the Bilateral Filter Sylvain Paris MIT CSAIL Blur Comes from Averaging across Edges * output input * * Same Gaussian kernel everywhere.
Sylvain Paris – MIT CSAIL
Same Gaussian kernel everywhere.
S
q q p
The kernel shape depends on the image content.
space weight
range weight
normalization factor
S
q q q p p p
r s
Same idea: weighted average of pixels.
pixel intensity pixel position
space
space range normalization
Gaussian blur
S
I I I G G W I BF
q q q p p p
q p | | || || 1 ] [
r s
Bilateral filter
S
I G I GB
q q p
q p || || ] [
input
S
I I I G G W I BF
q q q p p p
q p | | || || 1 ] [
r s
reproduced from [Durand 02]
S
q q q p p p
r s
Only pixels close in space and in range are considered.
s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r =
(Gaussian blur)
Exploring the Parameter Space
s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r =
(Gaussian blur)
Varying the Range Parameter
s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r =
(Gaussian blur)
Varying the Space Parameter
Noisy input Bilateral filter 7x7 window
Bilateral filter
Median 3x3
Bilateral filter
Median 5x5
Bilateral filter Bilateral filter – lower sigma
Bilateral filter
Bilateral filter – higher sigma