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

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

Scattering images

Frascati, Roma, March 26-28, 2012

Gonzalo Farias*, Sebastián Dormido-Canto, Jesús Vega, Ignacio Pastor, Matilde Santos

*School of Electrical Engineering at Pontificia Universidad Católica deValparaíso (PUCV), Valparaíso, Chile. e-mail: [email protected])

Fusion Data Processing Validation and Analysis

1. Introduction• The TJ-II Thomson Scattering Diagnostic• Stray-light• Possible solutions?

2. Approaches• Problem formulation• Exhaustive detection• Connected components• Region growing

3. Results• Typical algorithm used• Example of processing• Validation

4. Conclusions and Future Works

2/29Contents

1. Introduction• The TJ-II Thomson Scattering Diagnostic• Stray-light• Possible solutions?

2. Approaches• Problem formulation• Exhaustive detection• Connected components• Region growing

3. Results• Typical algorithm used• Example of processing• Validation

4. Conclusions and Future Works

3/29Contents

TJ-II Thomson Scattering diagnostic

4/29Introduction

The TJ-II TS diagnostic

collects five different types of images

5/29Introduction

BKG STR

ECH NBI COFF

TJ-II Thomson Scattering diagnostic (noise)

6/29Introduction

stray light (noise)

TJ-II Thomson Scattering diagnostic (noise)

7/29Introduction

stray light (noise)

The TJ-II TS diagnostic

collects five different types of images (revisited)

8/29Introduction

BKG STR

ECH NBI COFF

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:

1. Exhaustive detection

2. Connected components

3. Region growing

9/29Introduction

1. Introduction• The TJ-II Thomson Scattering Diagnostic• Stray-light• Possible solutions?

2. Approaches• Problem formulation• Exhaustive detection• Connected components• Region growing

3. Results• Typical algorithm used• Example of processing• Validation

4. Conclusions and Future Works

10/29Contents

Problem formulation using a toy example

11/29Approaches

Original image

Goal: Eliminate part of the image recognized as noise

noise

Exhaustive detection: how does it work?

Key idea: Use the template as sliding-window in order to find coincidences in the original image.

12/29Approaches

Original image

template

Exhaustive detection: results

Key idea: Use the template as sliding-window in order to find coincidences in the original image.

13/29Approaches

template

Original Processed

Exhaustive detection: comments

14/29Approaches

• 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.

Connected components: how does it work?

There are parts of the image where the components (pixels) are connected (no space between them). Connected pixels represent a region.

15/29Introduction

Original image

region 1

region 2

region 6

region 7

region 4

region 3

region 5

Connected components: how does it work?

Key idea: Eliminate a region (R) when some condition is satisfied.

16/29Introduction

Original image

region 1

region 2

Conditions for noise:•Position (R) is on left side

•Size(R) is >= 3 pixelsProcessed image

Connected components : comments

17/29Approaches

• 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.

Region growing: how does it work?

Regions are built by adding pixels. The addition is performed when the pixel meets some requirements (predicate).

18/29Approaches

Original image

region 1

region 2

region 4

region 5

region 3

Region growing: how does it work?

Key idea: Eliminate a region (R) when some condition is satisfied.

19/29Approaches

Original image

region 1

region 2

Conditions for noise:•Position (R) is on left side

•Size(R) is >= 3 pixelsProcessed image

Region growing: comments

20/29Approaches

• 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.

1. Introduction• The TJ-II Thomson Scattering Diagnostic• Stray-light• Possible solutions?

2. Approaches• Problem formulation• Exhaustive detection• Connected components• Region growing

3. Results• Typical algorithm used• Example of processing• Validation

4. Conclusions and Future Works

21/29Contents

Applying region segmentation to TS diagnostic: Algorithm of connected component approach

22/29Results

Applying region segmentation to TS diagnostic: Algorithm of region growing approach

23/29Results

Applying region segmentation to TS diagnostic: Connected components example

24/29Results

Applying region segmentation to TS diagnostic: Connected components example

25/29Results

Validation Radial profiles of the electron temperature

26/29Results

1. Introduction• The TJ-II Thomson Scattering Diagnostic• Stray-light• Possible solutions?

2. Approaches• Problem formulation• Exhaustive detection• Connected components• Region growing

3. Results• Typical algorithm used• Example of processing• Validation

4. Conclusions and Future Works

27/29Contents

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.

28/29Conclusions

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

Scattering images

Frascati, Roma, March 26-28, 2012

Gonzalo Farias*, Sebastián Dormido-Canto, Jesús Vega, Ignacio Pastor, Matilde Santos

*School of Electrical Engineering at Pontificia Universidad Católica deValparaíso (PUCV), Valparaíso, Chile. e-mail: [email protected])

Fusion Data Processing Validation and Analysis