Fixing the Gaussian Blur: the Bilateral Filter Sylvain Paris MIT - - PowerPoint PPT Presentation

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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.


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SLIDE 1

A Gentle Introduction to Bilateral Filtering and its Applications

“Fixing the Gaussian Blur”: the Bilateral Filter

Sylvain Paris – MIT CSAIL

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SLIDE 2

Blur Comes from Averaging across Edges

* * *

input

  • utput

Same Gaussian kernel everywhere.

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SLIDE 3

Properties of Gaussian Blur

  • Does smooth images
  • But smoothes too much:

edges are blurred.

– Only spatial distance matters – No edge term

input

  • utput

 

 

S

I G I GB

q q p

q p || || ] [

space

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SLIDE 4

Bilateral Filter No Averaging across Edges

* * *

input

  • utput

The kernel shape depends on the image content.

[Aurich 95, Smith 97, Tomasi 98]

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SLIDE 5

space weight

not new

range weight

I new

normalization factor

new

Bilateral Filter Definition: an Additional Edge Term

 

 

  

S

I I I G G W I BF

q q q p p p

q p | | || || 1 ] [

r s

 

Same idea: weighted average of pixels.

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SLIDE 6

Illustration a 1D Image

  • 1D image = line of pixels
  • Better visualized as a plot

pixel intensity pixel position

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SLIDE 7

space

Gaussian Blur and Bilateral Filter

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

[Aurich 95, Smith 97, Tomasi 98] space space range p p q q

 

 

S

I G I GB

q q p

q p || || ] [

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SLIDE 8

q p

Bilateral Filter on a Height Field

  • utput

input

 

 

  

S

I I I G G W I BF

q q q p p p

q p | | || || 1 ] [

r s

 

p

reproduced from [Durand 02]

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SLIDE 9

Space and Range Parameters

  • space s : spatial extent of the kernel, size of

the considered neighborhood.

  • range r : “minimum” amplitude of an edge

 

 

  

S

I I I G G W I BF

q q q p p p

q p | | || || 1 ] [

r s

 

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SLIDE 10

Influence of Pixels

p

Only pixels close in space and in range are considered.

space range

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SLIDE 11

s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r = 

(Gaussian blur)

input

Exploring the Parameter Space

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SLIDE 12

s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r = 

(Gaussian blur)

input

Varying the Range Parameter

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SLIDE 13

input

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SLIDE 14

r = 0.1

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SLIDE 15

r = 0.25

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SLIDE 16

r = 

(Gaussian blur)

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SLIDE 17

s = 2 s = 6 s = 18 r = 0.1 r = 0.25 r = 

(Gaussian blur)

input

Varying the Space Parameter

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SLIDE 18

input

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SLIDE 19

s = 2

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SLIDE 20

s = 6

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SLIDE 21

s = 18

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SLIDE 22

Basic denoising

Noisy input Bilateral filter 7x7 window

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SLIDE 23

Bilateral filter

Basic denoising

Median 3x3

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SLIDE 24

Bilateral filter

Basic denoising

Median 5x5

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SLIDE 25

Basic denoising

Bilateral filter Bilateral filter – lower sigma

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SLIDE 26

Bilateral filter

Basic denoising

Bilateral filter – higher sigma