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Image processing methods for noise reduction in the TJ-II Thomson Scattering images

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Fusion Data Processing Validation and Analysis Image processing methods for noise reduction in the TJ-II Thomson Scattering images Gonzalo Farias*, Sebasti n Dormido ... – PowerPoint PPT presentation

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Title: Image processing methods for noise reduction in the TJ-II Thomson Scattering images


1
Image processing methods for noise reduction in
the TJ-II Thomson Scattering images
Fusion Data Processing Validation and Analysis
Gonzalo Farias, Sebastián Dormido-Canto, Jesús
Vega, Ignacio Pastor, Matilde Santos
School of Electrical Engineering at Pontificia
Universidad Católica de Valparaíso (PUCV),
Valparaíso, Chile. e-mail gonzalo.farias_at_ucv.cl)
Frascati, Roma, March 26-28, 2012
2
  • Introduction
  • The TJ-II Thomson Scattering Diagnostic
  • Stray-light
  • Possible solutions?
  • Approaches
  • Problem formulation
  • Exhaustive detection
  • Connected components
  • Region growing
  • Results
  • Typical algorithm used
  • Example of processing
  • Validation
  • Conclusions and Future Works

3
  • Introduction
  • The TJ-II Thomson Scattering Diagnostic
  • Stray-light
  • Possible solutions?
  • Approaches
  • Problem formulation
  • Exhaustive detection
  • Connected components
  • Region growing
  • Results
  • Typical algorithm used
  • Example of processing
  • Validation
  • Conclusions and Future Works

4
  • TJ-II Thomson Scattering diagnostic

5
  • The TJ-II TS diagnostic
  • collects five different types of images

BKG
STR
ECH
NBI
COFF
6
  • TJ-II Thomson Scattering diagnostic (noise)

stray light (noise)
7
  • TJ-II Thomson Scattering diagnostic (noise)

stray light (noise)
8
  • The TJ-II TS diagnostic
  • collects five different types of images
    (revisited)

BKG
STR
ECH
NBI
COFF
9
  • Possible solutions?
  • Apply a hardware filter There is a Notch filter
    (a band-stop filter) in operation, which has a
    large stray-light rejection, but not all noise is
    eliminated.
  • Apply low-pass or advanced filters (e.g.
    wavelets), but this action will affect to entire
    images. This happens with all global filters.
  • Apply algorithms considering some particular
    characteristics of noise localization, area,
    density, and in general any kind of noise
    feature
  • Exhaustive detection
  • Connected components
  • Region growing

10
  • Introduction
  • The TJ-II Thomson Scattering Diagnostic
  • Stray-light
  • Possible solutions?
  • Approaches
  • Problem formulation
  • Exhaustive detection
  • Connected components
  • Region growing
  • Results
  • Typical algorithm used
  • Example of processing
  • Validation
  • Conclusions and Future Works

11
  • Problem formulation using a toy example

Original image
noise
Goal Eliminate part of the image recognized as
noise
12
  • Exhaustive detection how does it work?

Original image
template
Key idea Use the template as sliding-window in
order to find coincidences in the original image.
13
  • Exhaustive detection results

Original
Processed
template
Key idea Use the template as sliding-window in
order to find coincidences in the original image.
14
  • Exhaustive detection comments
  • Useful when the part of the image to look for
    (e.g. noise) is regular and well defined.
  • There is a lot of applications where this
    technique has excellent results optical
    character recognition, automatic number plate
    recognition, face and pedestrian detection, etc.
  • However the technique is not suitable for
    irregular parts such as the stray-light of TS
    diagnostic.

15
  • Connected components how does it work?

Original image
region 4
region 1
region 5
region 6
region 2
region 7
region 3
There are parts of the image where the components
(pixels) are connected (no space between them).
Connected pixels represent a region.
16
  • Connected components how does it work?
  • Conditions for noise
  • Position (R) is on left side
  • Size(R) is gt 3 pixels

Processed image
Original image
region 1
region 2
Key idea Eliminate a region (R) when some
condition is satisfied.
17
  • Connected components comments
  • Useful when the part of the image to look for
    (e.g. noise) is irregular and not-well defined.
  • The connected components or region extraction
    techniques are based on the image segmentation
    theory.
  • Very nice results on the noise reduction in the
    TS diagnostic (we will see later), but the
    predicate of connection for a pixel is too
    strong. Therefore, some pixels quite near, but
    not connected, to the region are not considered
    as noise in this approach.

18
  • Region growing how does it work?

Original image
region 3
region 1
region 4
region 2
region 5
Regions are built by adding pixels. The addition
is performed when the pixel meets some
requirements (predicate).
19
  • Region growing how does it work?
  • Conditions for noise
  • Position (R) is on left side
  • Size(R) is gt 3 pixels

Original image
Processed image
region 1
region 2
Key idea Eliminate a region (R) when some
condition is satisfied.
20
  • Region growing comments
  • Useful when the part of the image to look for
    (e.g. noise) is irregular and not-well defined.
  • The region growing is also based on the image
    segmentation theory.
  • Similar results on the noise reduction in the TS
    diagnostic as the previous approach, but the
    regions depend on the initial seeds selected.

21
  • Introduction
  • The TJ-II Thomson Scattering Diagnostic
  • Stray-light
  • Possible solutions?
  • Approaches
  • Problem formulation
  • Exhaustive detection
  • Connected components
  • Region growing
  • Results
  • Typical algorithm used
  • Example of processing
  • Validation
  • Conclusions and Future Works

22
  • Applying region segmentation to TS diagnostic
  • Algorithm of connected component approach

23
  • Applying region segmentation to TS diagnostic
  • Algorithm of region growing approach

24
  • Applying region segmentation to TS diagnostic
  • Connected components example

25
  • Applying region segmentation to TS diagnostic
  • Connected components example

26
  • Validation
  • Radial profiles of the electron temperature

27
  • Introduction
  • The TJ-II Thomson Scattering Diagnostic
  • Stray-light
  • Possible solutions?
  • Approaches
  • Problem formulation
  • Exhaustive detection
  • Connected components
  • Region growing
  • Results
  • Typical algorithm used
  • Example of processing
  • Validation
  • Conclusions and Future Works

28
  • Conclusions and future works
  • Fusion images processing can be benefits from
    region segmentation methods.
  • From observation of several experiments, both
    region segmentation methods seem to be promising
    in order to reduce stray-light.
  • Connected components approach is quite direct,
    and can be implemented easily, although is not so
    flexible.
  • Region growing is much more flexible, but
    selection of initial seeds is not direct.
  • Validation mechanisms seem confirm visual
    checking.

29
Image processing methods for noise reduction in
the TJ-II Thomson Scattering images
Fusion Data Processing Validation and Analysis
Gonzalo Farias, Sebastián Dormido-Canto, Jesús
Vega, Ignacio Pastor, Matilde Santos
School of Electrical Engineering at Pontificia
Universidad Católica de Valparaíso (PUCV),
Valparaíso, Chile. e-mail gonzalo.farias_at_ucv.cl)
Frascati, Roma, March 26-28, 2012
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