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 grating
Source: [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 sensor
RGB 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
development
Monolithic 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 (𝐿 𝑛)
LED White light

“Channels.data” calibration file
𝑃𝑜𝑖𝑛𝑡1
𝑃𝑜𝑖𝑛𝑡2
𝑤 𝑐ℎ
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 =
𝐴 𝑛
𝐴 𝑟
→ 𝑓𝑛 ∙ 𝐹 𝜆
𝐴: Area
𝑓𝑛: Scaling factor
𝐴 𝑛: Current channel area
𝐴 𝑟: 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
e.zarate@pucp.pe
mario-eduardo.zarate-caceres@tu-ilmenau.de
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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