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

Fusion Data Processing Validation and Analysis Image processing methods for noise reduction in the TJ-II Thomson Scattering images 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@ucv.cl) Frascati, Roma, March 26-28, 2012

Contents 2/29 • 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

Introduction 4/29 TJ-II Thomson Scattering diagnostic

Introduction 5/29 The TJ-II TS diagnostic collects five different types of images BKG STR NBI ECH COFF

Introduction 6/29 TJ-II Thomson Scattering diagnostic (noise) stray light (noise)

Introduction 7/29 TJ-II Thomson Scattering diagnostic (noise) stray light (noise)

Introduction 8/29 The TJ-II TS diagnostic collects five different types of images (revisited) BKG STR NBI ECH COFF

Introduction 9/29 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

Approaches 11/29 Problem formulation using a toy example Original image noise Goal: Eliminate part of the image recognized as noise

Approaches 12/29 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.

Approaches 13/29 Exhaustive detection: results Processed Original template Key idea: Use the template as sliding-window in order to find coincidences in the original image.

Approaches 14/29 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.

Introduction 15/29 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.

Introduction 16/29 Connected components: how does it work? • Conditions for noise: • Position (R) is on left side • Size(R) is >= 3 pixels Processed image Original image region 1 region 2 Key idea: Eliminate a region (R) when some condition is satisfied.

Approaches 17/29 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 predicateof 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.

Approaches 18/29 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).

Approaches 19/29 Region growing: how does it work? • Conditions for noise: • Position (R) is on left side • Size(R) is >= 3 pixels Original image Processed image region 1 region 2 Key idea: Eliminate a region (R) when some condition is satisfied.

Approaches 20/29 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.

Results 22/29 Applying region segmentation to TS diagnostic: Algorithm of connected component approach

Results 23/29 Applying region segmentation to TS diagnostic: Algorithm of region growing approach

Results 24/29 Applying region segmentation to TS diagnostic: Connected components example

Results 25/29 Applying region segmentation to TS diagnostic: Connected components example

Results 26/29 Validation Radial profiles of the electron temperature

Conclusions 28/29 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.

Fusion Data Processing Validation and Analysis Image processing methods for noise reduction in the TJ-II Thomson Scattering images 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@ucv.cl) Frascati, Roma, March 26-28, 2012

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