image quality indexes for infrared nondestructive...

Infrared Nondestructive Testing Seminar-March 16 2012

Image quality indexes for infrared nondestructive testing

Hernán Darío Benítez Restrepo

Pontificia Universidad Javeriana-Cali


1. Introduction

2. Digital image processing techniques in Infrared Nondestructive Testing IRNDT

3. Image quality metrics

4. IRIQI (Infrared Image Quality Index)

5. Results and analysis

6. Conclusions and further work

7. Acknowledgements

Outline


Introduction

Image acquisition Image processing

Image compression (i.e jpeg)

Transmitter Receiver

Image transmission Image display

Digital images are subject to a wide variety of distortionsany of which may result in a degradation of visual quality


Introduction

Impulsive Salt-Pepper Noise

Original

Additive Gaussian Noise Blurring JPEG Compression

How to predict automatically and objectivelythe perceived visual quality of these images ?

Source: https://ece.uwaterloo.ca/~z70wang/research/quality_index/demo_goldhill.html

https://ece.uwaterloo.ca/~z70wang/research/quality_index/demo_goldhill.html

https://ece.uwaterloo.ca/~z70wang/research/quality_index/demo_goldhill.html


An objective image quality metric can play a variety of roles in image processing applications:

1. Monitoring. i.e allocation of streaming resources in digital video server

2. Optimization of algorithms and parameters setting. i.e tuning of image filtering

3. Benchmarking of image processing systems and algorithms i.e comparison of infrared image processing techniques in terms of image quality.

Introduction


Introduction

What is the ‘best’ technique to improve image quality ?

Original First derivative

Principal component analysis

Phase image

Image of maximumcontrast

Second derivative


Introduction

Original Blurred image

Blurred image Original

Features of original image

Features of blurred image

Blurred image

Full reference

Relative reference

Reference free


Digital image processing techniques in Infrared Nondestructive Testing IRNDT

Transmission

1

Reflection

3

6

Analysis

2

Sample 4

IR camera

5

IR images


Thermal Contrast

Absolute contrast Ca

∆T = Ca = Td − Tnd

Normalized contrast Cn, tm

when the excess of temperature is maximal

∆T = Cn =Tdef (t)

Tdef (tm)− Ts(t)

Ts(tm)

50 100 150 200

50

100

150

200

250

Image of maximum normalized contrast (CFRP sample)



d

dtln(T ) =

N�

n=1

nanlnn−1(t)

d2

dt2ln(T ) =

N�

n=2

(n− 1)nanlnn−2(t)

First derivative

Second derivative

TSR (Thermographic Signal Reconstruction)


a0 a1 a2 a3 a4


Image quality metrics

UQI� �� Universal Quality Index

=σAB

σAσB� �� loss of correlation

∗ 2AB

(A)2 + (B)2� �� luminance distortion

∗ 2σAσB

σ2A + σ2

B� �� contrast distortion

SNR� �� Signal To Noise Ratio

=| ∆T |

σnon−defective

A Universal Image Quality Index, Z. Wang and A. Bovik, IEEE SIGNAL PROCESSING LETTERS, VOL. 9, NO. 3, MARCH 2002

Defense and illustration of time-resolved pulsed thermography for NDE, D. Balageas, QIRT Journal Vol 1 2012.

B. Girod, “What’s wrong with mean-squared error,” in Digital Images and Human Vision, A. B. Watson, Ed. Cambridge, MA: MIT Press, 1993, pp. 207–220

MSE� �� Mean Squared Error

=1

M ∗N�

M,N

(A(m,n)−B(m,n))2


50 100 150 200

50

100

150

200

250

IRIQI (Infrared Image Quality Index)

Image processing technique

i.e (TSR, Thermal contrast)

X ∈ [X1, X2] Y ∈ [Y1, Y2]

E = Y −X

E

Defective (ND) area

Non Defective (D) area

LIQI� �� Local Infrared Quality Index

=

�kn=1 | pD(n)− pND(n) |

�kn=1 | pD(n) | + | pND(n) |

CE��Contrast enhancement

= 1− 2σXσY

σ2X + σ2

Y� �� contrast distortion

IRIQI = LIQI ∗ CE


Results and analysisCFRP

(Carbon Fiber Reinforced Plastic)

Sampling frequency: 157 HzTime of acquisition: 6.3 s

Size of images sequence: 292 x 246 x 991

Defective area (1)

Defective area (2)

Sound area


Results and analysisNormalized Thermal Contrast

Row 1 - Defective area (1) - depth= 0.2 mmRow 2 - Defective area (2) - depth= 1.0 mm

0 1 2 3 4 5 6 70.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time(s)

Qua

lity

Inde

x [0

,1]

IRIQIUQI

0 1 2 3 4 5 6 720

10

0

10

20

30

40

Time(s)

dB

SNR

0 1 2 3 4 5 6 70.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Time(s)

Qua

lity

Inde

x [0

,1]

IRIQIUQI

0 1 2 3 4 5 6 720

10

0

10

20

30

40

Time(s)

dB

SNR

0 1 2 3 4 5 6 70

10

20

30

40

50

60

Time(s)

dB

MSE

0 1 2 3 4 5 6 70

10

20

30

40

50

60

Time(s)

dB

MSE


Results and analysisTSR

Second derivativeRow 1 - Defective area (1) - depth= 0.2 mmRow 2 - Defective area (2) - depth= 1.0 mm

0 1 2 3 4 5 6 70.4

0.2

0

0.2

0.4

0.6

0.8

1

Time(s)

Qua

lity

Inde

x [0

,1]

IRIQIUQI

0 1 2 3 4 5 6 720

10

0

10

20

30

40

Time(s)

dB

SNR

0 1 2 3 4 5 6 70

10

20

30

40

50

60

Time(s)

dB

MSE

0 1 2 3 4 5 6 70.2

0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Time(s)

Qua

lity

Inde

x [0

,1]

IRIQIUQI

0 1 2 3 4 5 6 720

10

0

10

20

30

40

Time(s)

dB

SNR

0 1 2 3 4 5 6 70

10

20

30

40

50

60

Time(s)

dB

MSE


Conclusions and further work

•Image quality assessment in IRNDT is important to objectively compare the visual results of techniques such as Pulsed Phase Thermography (PPT), TSR, thermal contrast, PCT (Principal Components Thermography), etc.

•IRIQI is self-reference and full- reference (i.e it needs the temperature image as reference).

•IRIQI can become a self-reference image quality index by comparing the contrast between defective and non-defective areas.

•IRIQI can become a global image quality index if statistical features are measured locally and then combined them together.


Acknowledgements

•CIAM 2009-2012 (Colaboración Interamericana de Materiales)

•COLCIENCIAS

•MIVIM (Multipolar Infrared Vision Infrarouge Multipolaire) Research Chair Canada


Thank you ! Questions ?

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