Master Thesis 2016

Image acquisition and processing

for multichannel spectral imaging

Submitted by

B.Sc. Mario Eduardo Zárate Cáceres

27.04.2016www.tu-ilmenau.deSeite 1

Dep. Quality Assurance and Industrial Image Processing, Fac. Mechanical Engineering

Responsible Professor (TU Ilmenau) Univ.-Prof. Dr. rer. nat Gunther Notni

Dipl.-Wirtsch.-lng. Edgar Reetz

Dr.-Ing. Martin Correns

Responsible Professor (PUCP)M.Sc. Ericka Madrid Ruiz

April, 2016 Ilmenau

Outline

1. Introduction

2. State of the Art

3. Implementation

4. Results

5. Limitations

6. Conclusions and Outlook


Motivation


• A wide spread field of applications for

spectrometers

• The market limits the range of applications

• Extend the range of applications as:

– Hand held devices

– Field application

– Low cost scenario

1. Introduction


Principle structure of diffraction gratingSource: [RCN15]

Unknown

optical

properties

Low

transmittance

+ UV

Spectrum

Spectra

orientation

Fluorescence Dye Marker


Optical fiber Imaging diffraction

grating

1280 x 1024 pixels

Monochrome

10 bits

Image sensorRGB Monochrome

Objectives


• Optimize the image acquisition

• Find and parameterize image regions

containing spectral information

• Compute spectra and display them

• Develop a calibration method

1. Introduction

Software

2. State of the Art


Miniaturized spectrometer

Array detector Matrix detector

Single channel Multi channel

Image

processing

Software

developmentMonolithic Miniature Spectral Sensor

for Multi-Channel Spectral Analysis

• 4 Channels

• Resolution 8 nm

• Comparatively cheap

Source: [RMB+06]

Source: [Ham15a]

Source: [Xim15a]

Low-cost

Mini-spectrometer

3. Implementation


A. Image Acquisition

B. Finding orientation

C. Image decomposition

D. Calibrating the wavelength

E. Cropping channels

F. Computing Spectra

G. Resizing spectrum per channel

Acquired image

𝛼

Test line (𝐿𝑛)

LED White light


“Channels.data” calibration file

𝑃𝑜𝑖𝑛𝑡1 𝑃𝑜𝑖𝑛𝑡2

𝑤𝑐ℎ

C. Image decomposition


𝛽“ValProChannels.data” file

Red laser

𝜆 = 650 𝑛𝑚

Green laser

𝜆 = 532 𝑛𝑚

D. Calibrating the wavelength

Relation

between

pixels and

wavelength

[nm/pixels]

E. Cropping channels


Scanning channels

(20 pixels per jump)


F. Computing Spectra

Original data

2D

Digital value

0-1023

Z-axis


Background subtraction


Data smoothing with filters


Channel

cropped in

𝒙′ = 𝟔𝟎𝟎


Channels according

to Gaussian Mean

Channel 6 according

to different methods

Considers all the points per

slide giving a fix weight

depending on their position

G. Resizing spectrum per channel

• Area scaling approach


𝐴 =

𝜆=380

𝜆=780

𝐹(𝜆)

∴ 𝑓𝑛 =𝐴𝑛𝐴𝑟

→ 𝑓𝑛 ∙ 𝐹 𝜆

𝐴: Area

𝑓𝑛: Scaling factor

𝐴𝑛: Current channel area

𝐴𝑟: Channel 6 area

“scale.data” calibration file


Rescaled channels according to Gaussian Mean

4. Results


Source: http://oceanoptics.com/product/hg-1/ HG-1 Mercury Argon lamp


Spectra using HG-1 Mercury Argon Calibration Light source

Online view


Image

11

Channels

Spectrum

Current

channel


5. Limitations

A line emission produces

a circle of approximately

60 pixels which

represents 30 nm

Spectrum HG-1

≈ ∅60 pixels

546.08 𝑛𝑚


Mechanical

problems

Image sensor

Screw

Diffraction

grating

Plastic base

6. Conclusions


• Compute spectra is possible using image matrix

detector

• The multi-spectrometer needs a reference light to be

calibrated

• The wavelength range depends on FOV, although it

could be changed due to assembly problems

• The wavelength range is between 400nm and 800nm

(VIS) with a estimated resolution of 30nm.

• The multi-spectrometer has a estimated measurement

uncertainty of±5nm

Future works and

Further development

• Optimize the code, reduce time consumption and

display spectra in real time

• The algorithm can be improved and tested in mobile

phones, taking advantages from cameras sensor

• Propose another wavelength calibration process with a

new approach, the linear approach was first done

• Work on an intensity calibration


Vielen Dank für Ihre

Aufmerksamkeit!!


Thank you for

your attention!!

Contact:

B.Sc. Mario Eduardo Zárate Cáceres

[email protected]

[email protected]


Special thanks to:

[RCN15]

[RMB+06]

[Ham15a]

[Xim15a]


Reetz, Edgar ; Correns, Martin ; Notni, Gunther: Cost effective spectral

sensor solutions for hand held and field applications.

Rosenberger, Maik ; Margraf, Jörg ; Brücknerl, Peter ; Töpferl,

Susanne ; Linß, Gerhard: Monolithic Miniature Spectral Sensor for Multi-

Channel Spectral Analysis. 00 (2006), S. 398–403

Hamamatsu: Advances in CMOS image sensors open doors to many

applications. http://www.hamamatsu.com/sp/hc/osh/osh_013_

002_figure02.jpg. Version: 2015. – Accessed: 16.02.2016

Ximea: Board level cameras - USB3 Vision. http://www.

lambdaphoto.co.uk/media/catalog/product/cache/1/

small_image/200x/9df78eab33525d08d6e5fb8d27136e95/

b/r/brd.jpg. Version: 2015. – Accessed: 12.11.2015

Bibliography

Live test


Wave length

522-542 nm

Master Thesis 2016

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