Using High Speed Shutter to ReduceMotion Blur in a Microbolometer

Aevan Nadjib Danial

Lulea University of TechnologyDept. of Computer Science, Electrical and Space Engineering

November 2013

ABSTRACT

Thermal cameras are widely used in many applications, They variate in price and perfor-

mance. The variant discussed in this paper is a thermal camera with a microbolometer

detector technology. Extremely affordable, lightweight and low power consumptions are a

few of the reasons why microbolometers are a good choice for every day thermal imaging

applications.

The main issue with a microbolometer camera is the motion blur effect shown if viewing

dynamic scenes, due to the thermal inertia of the detecting material. The aim of this

work is to reduce the motion blur effect with the help of high speed shutter technology.

An optical chopper is used to manipulate the exposure time for the camera during

operation. With the help of advanced software, data are collected for different exposure

times. The results show that the motion blur effect can be considerably reduced while

still maintaining sufficient amount of thermal information and image quality.

iii

PREFACE

This bachelor thesis is the final project for a bachelor degree in Engineering Physics and

Electrical Engineering at Lulea University of Technology and was conducted at Saab

Dynamics in Linkoping.

I would like to thank my supervisor at LTU, Torbjorn Lofqvist and my external super-

visor at Saab Dynamics, Arvid Larsson for their guidance and support. I would also like

to thank Termisk Systemteknik for providing the FLIR A35 thermal camera.

Aevan Nadjib Danial

v

CONTENTS

Chapter 1 – Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Problem area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Chapter 2 – Theory 3

2.1 Infrared radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Blackbody radiation . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.2 Atmospheric influence . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Resistive bolometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Performance parameters . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Microbolometer assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.1 Microbolometer temperature sensing materials . . . . . . . . . . . 6

2.4 Microbolometer operation . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4.1 Signal decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4.2 Motion blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Chapter 3 – Method 11

3.1 The equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 Thermal camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.2 Optical chopper . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.3 Oscilloscope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.4 Radiation sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.5 Complete assembly . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.6 Modified chopper blade . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 The experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 Step response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.2 Motion blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 4 – Results and Discussion 19

4.1 Object signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1.1 Step response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1.2 Time constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1.3 Temperature sensitivity . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Motion blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2.1 Image quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2.2 Signal memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chapter 5 – Future Work and Conclusion 27

5.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

viii

CHAPTER 1

Introduction

Infrared Radiation Imaging technology is widely used in many applications. The ever

increasing demands from military as well as non-military industries are driving the tech-

nology forward. Some of the common applications of infrared imaging are night vision,

fire fighting, research, medical imaging and surveillance.

Infrared camera detectors are divided into two varieties: cooled and uncooled. The

detector discussed in this paper is an uncooled variant called a microbolometer, a spe-

cific type of resistive bolometer. A resistive bolometer measures the electrical resistance

change when incident electromagnetic radiation strikes the detecting material [1]. The

microbolometer uses the same principle and does not require cooling unlike other types

of infrared detectors.

The typical spectral range for a modern microbolometer is between 7.5-13.5 µm and

image resolutions ranging from about 160×120 pixels up to 1024×1024 pixels [2], this is

made possible by the development of smaller pixel size, also called detector pitch between

15-50 µm [3]

1.1 Background

The first bolometer was invented by Samuel P. Langley in 1880, consisting of two platinum

strips covered with soot, one of the strips was protected from radiation and the other

one exposed to it, the strips formed a Wheatstone bridge and was fitted with a sensitive

galvanometer connected to a battery, the galvanometer produced a variation proportional

to the intensity of radiation falling on the exposed strip [4]. By the time Langley had

perfected his bolometer it was sensitive enough to be able to detect thermal radiation

from a cow 400 meters away [5].

Microbolometer technology was originally developed by Honeywell, Honeywell later li-

censed their technology to several manufacturers including FLIR Systems, InfraredVision

Technology Corporation, Boeing and BAE Systems. Uncooled infrared detectors have

1

2 Introduction

also been developed by European companies as for example ULIS.

1.2 Problem areaLightweight, small, low power consumption and relatively inexpensive compared to cooled

infrared cameras are a couple of advantages making the microbolometer an easy choice for

every day thermal imaging applications. Modern microbolometer utilize vanadium oxide

(VOx) or amorphous silicon (α-Si) as the thermal detecting material, both materials have

a defined thermal inertia which determine the sensitivity and the speed of the camera,

which is the main disadvantage compared to cooled infrared detectors when dealing with

situations where motion blur emerge caused by objects moving at high speed. Finding a

solution to this problem is the main reason for this work.

1.3 PurposeInvestigations were conducted to:

• Plot the step response of the microbolometer to compare with theoretical behaviour.

• Identify the time constant for a specific camera (FLIR A35) to compare with the

time constant supplied by the manufacturer.

• Investigate the temperature sensitivity when identifying objects with different tem-

peratures at decreased exposure time.

• Investigate the improvement of motion blur when decreasing the exposure time.

• Determine how the camera signal (image quality) is influenced by modifying the

exposure time.

• Analyse the ”signal memory” behaviour of the camera at extremely fast chopping

speed.

CHAPTER 2

Theory

2.1 Infrared radiation

Infrared (IR) light is a portion of electromagnetic radiation with wavelength longer than

visible light ranging from 0.7 to 1000 µm, in 1800 William Herschel discovered infrared

light in sunlight by using a prism and holding a thermometer just beyond the red light,

coming to a conclusion that there is invisible light beyond the visible spectrum.

Earth’s climate is greatly effected by infrared radiation which is approximately half

of the arriving energy from the sun’s radiation making life possible on earth, the rest

of the radiated energy is from visible light and ultraviolet radiation. Infrared radiation

is commonly divided into five divisions, see table 2.1. The most important one in this

work is the long-wavelength infrared (LWIR), also called the ”thermal imaging” region

where collected temperature values of an object by an IR camera are converted into an

calibrated digital image, therefore displaying an objects temperature without making

contact.

Table 2.1: Divisions of infrared radiation

Division Abbreviation Wavelength

Near infrared NIR 0.7-1.4 µm

Short-wavelength infrared SWIR 1.4-3 µm

Mid-wavelength infrared MWIR 3-8 µm

Long-wavelength infrared LWIR 8-14 µm

Far infrared FIR 14-1000 µm

3

4 Theory

2.1.1 Blackbody radiation

A blackbody is characterised as a perfect radiator, absorbing all incident radiation falling

upon it. In 1900 Max Planck announced an equation describing the relation between the

radiation emitted by a blackbody as a function of temperature and wavelength [6], the

relation is shown here:

Wλ(T ) =2hc2

λ51

ehc

λkBT − 1(2.1)

where

• Wλ(T ) is the spectral radiance as a function of wavelength

• h is the Planck constant

• kB is the Boltzmann constant

• T is the temperature in Kelvin

• c is the speed of light in vacuum

Equation 2.1 is called Planck radiation law.

Another equation explaining the total radiation emitted by a blackbody shown here:

P = εσAT 4 (2.2)

where

• ε is the emissivity of the radiating object ranging from 0 to 1 giving information

about how close is the object radiating as an perfect blackbody, denoting Pobject as

the radiation emitted by an object and Pblack the radiation emitted by a blackbody

will give equation 2.3

ε =PobjectPblack

(2.3)

• σ is the Stefan-Boltzmann constant

• A is the area of the radiating object

Equation 2.2 is the Stefan-Boltzmann law which is valid for objects having a con-

stant value for emissivity at any wavelength, also called a greybody, but objects usually

identified by an infrared camera are neither blackbodies nor greybodies. However, ther-

mography is conducted only at specific range of wavelength making it possible to treat

objects as greybodies, acknowledging the emissivity is therefore a very important part of

thermal imaging.

2.2. Resistive bolometers 5

2.1.2 Atmospheric influence

Life is sustainable on earth because of the atmospheric transmittance, or atmospheric

window where infrared radiation is allowed by the atmosphere, without it earth will

be much too warm to support life. There are two important parts of this atmospheric

window that have to be acknowledged when conducting thermographic measurements,

they are the mid-wavelength window ranging between 2-5 µm and the long-wavelength

window ranging between 7.5-13.5 µm. The atmospheric transmittance prevents an in-

frared camera from collecting the total radiation from an object, therefore correction for

atmospheric attenuation are made with the help of IR software to display better results.

2.2 Resistive bolometers

A resistive bolometer can be simply explained as a temperature-sensitive electrical re-

sistor, the detecting material temperature is raised by the incident radiation, the tem-

perature raise causes a change in electrical resistance measured by an external circuit.

When radiation is no longer falling upon the detector, temperature decreases at a rate

determined by performance parameters mentioned below.

2.2.1 Performance parameters

The performance of any resistive bolometers is marked by these most important param-

eters:

• Signal-to-noise ratio (SNR). A value obtained by comparing the average object

signal (µ) to the standard deviation of the noise (σ). Shown in equation 2.4.

SNR =µ

σ. (2.4)

• Noise Equivalent Temperature Difference (NETD). It is the amount of detected

temperature signal required to match the internal noise of the detector, resulting

in a SNR value of 1.

• Responsivity (<), a measurement of how well the detector is able to convert the

incident optical signal into an electrical signal.

• Thermal response time (τ), also labelled time constant. The time it takes the

detector to reach 1− 1e≈ 63% of the final value, in response to a step signal input

[7].

6 Theory

2.3 Microbolometer assembly

When designing and manufacturing a microbolometer, a number of criteria and design

features are considered to meet the performance standards, modern microbolometers

employ a pixel structure originally developed by Honeywell. To produce the detecting

area, a thin membrane usually made of silicon nitride (Si3N4) is applied mainly because

of its insulator property, upon which the detecting material usually being vanadium

oxide (VOx) is dispersed. To transfer the electrical change to the read-out electrical

circuit, the membrane are supported above a silicon substrate with long and low thermal

conductive ”legs” typically made of thin metal. The legs are long to obtain a small

thermal conduction between the detector and its surrounding. To maximize the amount

absorbed radiation, a reflective layer is placed on the substrate below the membrane to

reflect back the incident infrared radiation not fully absorbed by the membrane, the best

results are accomplished when the distance between the reflective layer and the membrane

is λ4

[1], therefore a large amount of the incident radiation at a specific wavelength λ is

absorbed. If the detector is operating in the 8-12 µm spectral interval, the spacing would

be between 2-3 µm.

Each individual pixel is placed in an array to create the so called focal plane array

(FPA). In addition, the assembly is integrated into a vacuum package to minimize the

thermal conduction between the microbolometer and its surrounding. The structure of

a pixel and the focal plane array are seen in figure 2.1

The structure of the focal plane array of a microbolometer is in rapid development

with both increased image resolution and decreased pixel dimensions. As mentioned in

chapter 1, companies are producing low-cost detectors with a detector pitch as small as

17 µm making it possible today to increase the resolution as high as 1024× 1024 pixels.

2.3.1 Microbolometer temperature sensing materials

The Material used to sense temperature changes in a microbolometer is very important,

the sensitivity of such material will decide the image quality and depth of view. The two

most used materials are: vanadium oxide (VOx) and amorphous silicon (α-Si). In the

early days of microbolometer technology,α-Si had an advantage over VOx because of its

ability to be manufactured it in a silicon foundry, meanwhile vanadium oxide technology

was still controlled by the US military. Today, there are no reasons to choose α-Si

over VOx since it is produced in a silicon foundry and the licence restriction have been

removed.

It is important to compare the NETD value for these two materials. A low NETD

value means that a thermal camera will be able to detect extremely small temperature

differences, resulting in more detailed images and a better range performance. Companies

producing these detectors provide a measured NETD values at different parameters,

one of these parameters is the F-number of the lens, which is the ratio of the lens’s

focal length to the diameter of the entrance pupil. A recalculation of the F-number is

2.4. Microbolometer operation 7

Figure 2.1: The structure of the individual pixels in a microbolometer with an illustration of

the focal plane array.

therefore necessary to achieve the same parameter value before comparing the NETD

values. Detectors using VOx are more sensitive than ones using α-Si [3].

2.4 Microbolometer operation

The image processing of a microbolometer is involved around a typical step response of

first order for any given system. First, a Heavyside step function is introduced when the

detector is subjected to a temperature difference, a step response will immediately build

up with a specific speed. The speed is often specified according to the time constant

which is the time it takes to reach 1− 1e≈ 63% of the initial input value. It is important

not to mix time constant with the integration time since a microbolometer is constantly

open to incoming radiation, and responds to changes with the specified time constant,

the term ”integration time” is used to investigate the behaviour of the detector when

8 Theory

externally changing the amount of time where a step response build up is allowed. The

step response and time constant for a microbolometer are explained with [1]

Cd∆T

dt+G∆T = ηβADP0[(h(0)− h(ti)], (2.5)

τ =C

G. (2.6)

The parameters in equation 2.5 are listed below.

• η is the optical absorption coefficient.

• β is the fill factor, defines the portion of the pixel area which is used to absorb the

incident radiation.

• AD is the pixel area.

• P0 is the amplitude of the incident radiation power per unit area which is modulated

at angular frequency ω.

• h is the Heavyside step function with ti as the integration time.

• G and C is the thermal conductance and heat capacity of the microbolometer.

The theoretical behaviour for a microbolometer is determined by solving and normal-

izing the differential equation 2.5, the results are seen below.

d∆T

dt+G

C∆T = 1, (2.7)

y = 1− e−tτ . (2.8)

Figure 2.2 display the theoretical step response for a microbolometer with a thermal

time constant of 15 ms.

2.4.1 Signal decay

When radiation reaching the detector is abruptly blocked at any specific moment, the

image signal level generated in that amount of time start to decay at an exponential

rate. The speed of the decay are dependent by the performance parameters explained

earlier, a collected term for this is often called the thermal inertia which is effected by

the responsivity, heat capacity and thermal conductance of the detecting material. The

fact that the a microbolometer is constantly open to incoming radiation and image signal

decay at a exponential rate makes uncooled thermal cameras inaccurate as a high speed

”snapshot” camera because moving objects map to more pixels resulting in a motion blur

effect.

2.4. Microbolometer operation 9

0 5 10 15 20 25 30 35 40 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [ms]

norm

aliz

ed s

igna

l

Time constant = 15ms

Figure 2.2: The theoretical step response for a microbolometer with thermal time constant 15

ms.

2.4.2 Motion blur

A solution to reduce the motion blur effect would be to shorten the amount of time

(exposure time) the radiation is falling upon the detector by for example using an optical

chopper, therefore reducing the amount of photons being detected. The major drawback

of this would be the lower level of image signal generated in that short time resulting in

a lower image quality, but the method would theoretically reduce the motion blur.

CHAPTER 3

Method

3.1 The equipment

Equipments used to perform the desired experiments are explained in this section.

3.1.1 Thermal camera

The main component being the camera is FLIR A35, it is highly temperature sensitive,

low cost and compact. The camera was controlled by a computer software called FLIR

ResearchIR MAX via a Ethernet cable. The image and optical data for FLIR A35 can

be seen in table 3.1. FOV is the range of angles from which the incident radiation can

be collected by the detector and IFOV is the range of incident angles seen by a single

detecting element in the focal plane.

Table 3.1: Specification for FLIR A35 camera [8]

Property Data

IR resolution 320× 256 pixels

NETD 50 mK

Field of view (FOV) 25°× 19°Spectral resolution (IFOV) 1.32 mrad

Focal length 19 mm

F-number 1.25

Detector type Focal plane array (FPA), uncooled (VOx) microbolometer

Spectral range 7.5-13 µm

Detector pitch 25 µm

Detector time constant Typical 12 ms

11

12 Method

3.1.2 Optical chopper

There are many ways to manipulate the amount of radiation reaching the camera, the

best way would be to use an optical shutter with configurable exposure time (shutter

speed). For the sake of using equipment already available in the lab, an optical chopper

was used instead. An optical chopper is a device which interrupts an incoming radiation

periodically, this is done with a rotating blade with an number of slots. The chopper

controller SR540 by Stanford Research Systems is displayed below.

Figure 3.1: The Optical chopper SR540 with a 5/6 slots rotating blade.

To further understand how the controller calculates the frequency of the rotating blade,

A small notch and a number of slots are viewed in figure 3.1, the controller determine

the outer and inner frequency by two light sensors detecting light when a notch and each

of the slots respectively pass over.

3.1.3 Oscilloscope

The frequency of the rotating blade could be easy viewed in the small display of the

SR540 controller, to gain a more accurate and a better representation of the frequency,

an oscilloscope was used alongside the controller. A coaxial cable is connected to the

reference port of the controller to send the signal. The oscilloscope used in this lab is

Agilent InfiniiVision DSO-X 3034A.

3.1.4 Radiation sources

To gain more accurate results when conducting the experiments, blackbody radiators

were used. A cavity blackbody IR-564 by Infrared System Development and a extended

3.1. The equipment 13

area blackbody PCN 300 by HGH Infrared Systems are displayed in figure 3.2, The

two blackbodies were also controlled to a precise temperature by their respective control

boxes.

Figure 3.2: From the left: the cavity blackbody and the extended area blackbody.

Each one of the blackbodies was set to different temperature to gain a temperature

difference of 10°C, the reason was to be able to find the temperature sensitivity for the

camera at reduced exposure time.

3.1.5 Complete assembly

A final view of the complete assembly can be seen in figure 3.3. The reference output

signal from the chopper controller was split to send signal to both the oscilloscope and

the cameras digital IO port. The connection to the camera was established by modifying

the cameras synchronizing cable end with a coaxial connector.

The motivation for the signal splitting was to achieve a synchronization between the

camera and the rotating chopper blade which could be done according to the camera

manual [9], both the camera and the rotating chopper are therefore operating at the

same frequency generating a frame each time the slot fly by the camera lens. However,

this is software limited at only (25, 30, 50 and 60) Hz. This is achieved by modifying the

outer light sensor notch detection frequency and aligning the camera with the beginning

14 Method

of the slot.

Figure 3.3: A complete assembly of the camera equipment.

3.1.6 Modified chopper blade

A five slot rotating blade was used mainly because the cut-out of the slot do not obstruct

the camera aperture, in addition to this, the blade was modified by covering four of the

slots with material obstructing the camera vision, in this case paper on each side of the

blade was glued on and cut to a specific shape making sure that the light sensors were

still detecting the notch and the slots, this is visible in figure 3.3.

The modification increases the amount of time the radiation is being blocked. Camera

signal decay at an exponential rate, it is therefore important that an ”old” step response

decayed an expectable amount before introducing the next one. The modification will

in this case regulate both the exposure time and the time between each exposure (signal

decay time) since it is a rotating blade. If a new signal is introduced earlier beacuse of

3.2. The experiments 15

the signal decay time not being long enough, partial of the old signal would be apparent

in the new signal, which in this text will be termed as ”signal memory”. A visualisation

of modified chopper blade operation can be seen in figure 3.4.

Figure 3.4: The modified chopper blade operation.

3.2 The experiments

A detailed walkthrough of the experiments conducted to solve the challenges listed in

chapter 1 are in this section.

3.2.1 Step response

The camera and the rotating chopper blade were mounted as close as possible on a optical

table board to minimize radiation reflection caused by the paper which could affect the

final results, also placing the two blackbodies at the same distance from the camera to

fully utilize its field of view. As earlier mentioned, a temperature difference for the two

blackbodies of 10°C was accomplished by their corresponding control boxes, noting that

a temperature stabilization can approximately take one hour.

The camera was connected to a computer via an Ethernet cable to be controlled by

the software FLIR ResearchIR MAX, with the two blackbodies radiating at different

temperature as a live feed on the display. Output data information can be set as real

temperature value or as a raw object signal. Object signal was chosen to achieve a wider

range of output data for better results. The software display content normally as a grey

scale, more detailed image is gained by selecting other palette for image colourization,

rainbow was therefore set as the standard colour scale, one can more easy distinguish a

16 Method

temperature difference across the whole scene or at a concentrated area of the camera

resolution with this palette.

To investigate the object signal from the two blackbodies, a measurement area was set

by the measurement tools embedded in the software on the two concentrated radiation

spots, This will measure both the pixel area and object signal value variation during

a time range. For the extended area blackbody, both average and standard deviation

values were acquired. As for the cavity blackbody, collecting the average values suf-

ficed since the extended area blackbody cover a larger measurement area and having a

higher temperature resulting in a overall more accurate data. Figure 3.5 displays the two

blackbodies with rainbow colourization.

Figure 3.5: A thermal image of the two blackbodies.

Data collection have to be made for different exposure time values to inspect the objects

signal variance, this is done by controlling the speed of the rotating chopper blade to

variate the exposure time between 1.5-45 ms with appropriate increments monitored

accurately with the oscilloscope. A maximum and minimum signal during unobstructed

versus fully blocked by the chopper blade was also obtained to be used as reference points.

Each data acquisition continued for approximately one minute and exported to a CSV

file.

Next, a MATLAB script file was written to import the information from the CSV files,

3.2. The experiments 17

the time constant of the camera can be identified by plotting the normalized step response

and identify the time at which ≈ 63% of the final value have been gained or by adding and

comparing with theoretical step response plots for different time constants. Furthermore,

finding the SNR values will give information about the sensitivity of the detector when

identifying two object with a temperature difference, subtracting the maximum signal

difference for the two objects and dividing with the corresponding standard deviation

will give the SNR value.

3.2.2 Motion blur

As mentioned in chapter 2, decreasing the exposure time will theoretically result in

an improvement for the motion blur effect when observing a moving object. This was

examined by using the extended area blackbody as the ”warm” background and dropping

a ”cold” steel ball from a specific height. This was executed for exposure times from 45

ms to 4 ms with appropriate decrements closely monitored by the oscilloscope. The large

temperature difference between the background and the steel ball simplified motion blur

detection, using a rainbow colourization assisted for an even easier detection.

Capturing an acceptable image of the falling steel ball was difficult especially at longer

exposure time since the radiation is being blocked longer then unobstructed because of the

chopper blade modification, a container was therefore placed to collected the falling ball

and the dropping procedure repeated multiple times while capturing a video sequence.

The video sequence was examined for possibly acceptable frame where the motion blur

of the ball is fully detected. To find the motion blur improvement at decreased exposure

time, a measurement area was placed covering the motion blur, pixel points was also

placed at the dropping hight, centre of the steel ball and the end of the extended area to

compensate for speed difference since the object can be captured at different position.

Finally, the original pixel length of the steel ball was found by capturing an image of

the ball while holding it in front of the background. The maximum motion blur was also

collected by dropping the ball when totally unobstructed by the chopper blade to be set

as a reference value. The collected data was exported to CSV files and processed with

MATLAB to find the motion blur improvement.

Image quality will be affected by shortening the exposure time, images for each time

exposure was therefore saved with a object signal scale. Figure 3.6 shows the motion

blur and the object signal scale for unobstructed capturing.

18 Method

Figure 3.6: Image displaying the motion blur for unobstructed capturing.

The signal memory appear at extremely fast chopping speed, a video sequence was

therefore taken at an exposure time of 1 ms. The behaviour was saved as a image to be

analysed.

CHAPTER 4

Results and Discussion

4.1 Object signal

Plotting the object signal obtained during the experiments for both the cavity and ex-

tended area blackbody will display the temperature difference, see figure 4.1. It is seen

that as exposure time decreases, the level of object signal also decreases and the temper-

ature difference fades out.

0 5 10 15 20 25 30 35 40 453000

4000

5000

6000

7000

8000

9000

Exposure Time [ms]

obje

ct s

igna

l

extended area blackbodycavity blackbody

Figure 4.1: The object signal for the two blackbodies.

19

20 Results and Discussion

4.1.1 Step response

The object signal values are converted temperature values, it is therefore necessary to

normalize them before a comparison to the theoretical behaviour can be done. This will

create a ”signal contrast” which can also be used to identify the image quality at any

exposure time and the time constant which will be, as mentioned before in chapter 3,

≈ 63% of the final value.

The maximum value was chosen from each data collection to ensure the highest object

signal, the normalization of these can be seen in the equation below.

contrast =Signalmax − Signalminmax

Signal(max− 1)max − Signalminmax· Signal(max− 1)max

Signalmaxmax. (4.1)

Where

• Signalmax is the highest value for each exposure time.

• Signalminmax is the highest value during fully blocked radiation.

• Signal(max− 1)max is the highest value for the exposure time of 45 ms.

• Signalmaxmax is the highest value during fully unobstructed radiation.

This should normalize all the object signal, which is confirmed with figure 4.2. It is

also visible that the signal reaches close to maximum level for the first 45 ms and an

almost identical step response for camera when viewing objects at different temperature.

4.1.2 Time constant

According to table 3.1, the time constant for the camera should be ”typical” 12 ms.

Roughly measuring the time for which an ≈ 63% of the final value have been reached

in figure 4.2 would result in 15 ms. Plotting the normalized theoretical step response in

equation 2.8 for time constants 12 and 15 ms and adding them presents figure 4.3 which

confirms the roughly estimated value.

4.1. Object signal 21

0 5 10 15 20 25 30 35 40 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Exposure Time [ms]

obje

ct c

ontr

ast

Extended area blackbodyCavity blackbody

Figure 4.2: The step response for FLIR A35 camera.

0 5 10 15 20 25 30 35 40 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Exposure time [ms]

obje

ct c

ontr

ast

Extended area blackbodyCavity blackbodyTheoretical data 12 msTheoretical data 15 ms

Figure 4.3: The theoretical step response for time constants 12 and 15 ms added.

22 Results and Discussion

4.1.3 Temperature sensitivity

For each maximum object signal collected, the correspondent standard deviation was

also collected. Denoting (EAB) for the extended area blackbody and (CB) for the cavity

blackbody results in equation 4.2. At an SNR value of 1, the temperature difference is

equal to the internal noise of the camera, meaning that the camera is unable to detect

any temperature difference between the two objects.

SNR =Signal(EAB)max − Signal(CB)max

σcorr. (4.2)

For a temperature difference of 10°C, figure 4.4 displays a SNR value of 1 for an exposure

time shorter then 4 ms. There are also values below 1, this is due to the high uncertainty

of the values collected at such high speed chopping, the values are small and differentiate

loosely.

0 5 10 15 20 25 30 35 40 450

2

4

6

8

10

12

Exposure Time [ms]

Sig

nal−

to−

nois

e ra

tio S

NR

Figure 4.4: A plot for the SNR values at different exposure times.

As a conclusion, the camera is loosing important temperature information when short-

4.2. Motion blur 23

ening the exposure time. Generally, exposure time would have to be shorter to reach

the temperature sensitivity limit for a higher temperature difference and vice versa. It is

important to set a boundary for how short the exposure time is allowed to be depending

on how temperature variant a scene to gain useful thermal images.

4.2 Motion blur

Dropping a steel ball in front of a much warmer background being the extended area

helped detecting the motion blur effect especially at decreased exposure time affecting

the sensitivity performance of the camera. The chance of capturing a justified image

where the motion blur is fully visible was very small for long exposure times which

lead to multiple video sequences. Figure 4.5 shows the motion blur improvement for a

decreasing exposure time. It is seen that the motion blur reduces with shorter exposure

time, while important information about the temperature differences across a scene is

lost.

(a) Unobstructed (b) 25 ms

(c) 8 ms (d) 4.5 ms

Figure 4.5: The motion blur improvement for decreased exposure time.

24 Results and Discussion

Compensating for speed difference because of the steel balls different position for each

video sequence, removing the original length of the steel ball in pixels and plotting the

affective motion blur results in the following figure below. At an exposure time of 4.5

ms, the motion blur is barely 3 pixels long which is a huge improvement.

0 5 10 15 20 25 30 35 40 450

5

10

15

20

25

30

35

Exposure time [ms]

mot

ion

blur

[pix

els]

Figure 4.6: A plot for the motion blur in pixels at different exposure time.

4.2.1 Image quality

The downside of shortening the exposure time is the loss of a detailed image. Viewing

figure 4.2 and the figure 4.5, it is concluded that image quality degrade by loss of sharp-

ness, temperature sensitivity and depth of view. There are however enough details to

suffice for most imaging situations.

4.3. Discussion 25

4.2.2 Signal memory

Because of a slow signal build up and an exponential signal decay, a microbolometer is

not intended to be used as a high speed camera. Due to the method used in this work,

an extremely short exposure time leads to a too short time period for the signal to decay.

This results in multiple ”ghosts” of an object in the same image frame if it is moving at

high speed. Figure 4.7 illustrate the steel ball repeated multiple times during an exposure

time of 1 ms. It is also apparent that ”repeats” create a cooler region where the two

intersect, yet another confirmation for signal memory behaviour.

Figure 4.7: An image of the moving steel ball at exposure time 1 ms (this figure produces poorly

if printed in black & white).

4.3 Discussion

It could be seen that all the experiments were conducted up to a certain exposure time

which in this case was 45 ms, this was not the case during the early stage of data collection

where the longest exposure time was set to 30 ms. To reach a step response of nearly the

maximum object signal obtained during unobstructed radiation, longer exposure time

26 Results and Discussion

is required, 30 ms was simply not enough to illustrate the ”levelling out” behaviour

characterizing the response to a step of first order.

Confusion may develop when viewing a exposure time 0 ms which is impossible, but

this value was assigned for the fully blocked radiation. Figure 4.1 displays an object

signal of ≈ 3500 as the ”dark count”, because the thermal cameras ability to ”always”

detect radiation in spite of how small it would be. This is however the best way to

approximate the smallest value obtained from the radiating objects.

The time constant for the camera, according to the specification, a ”typical” 12 ms.

Further investigation lead to a longer time constant of 15 ms which should be expected

since the company do not state a well-defined value.

As assumed, the shortening of the exposure time will effect the motion blur significantly,

but with a disadvantage as decreased image quality, figure 4.5 shows a reduction in image

contrast and depth of view. As an example, notice how the hand at the top ”blend in”

with the background as the exposure time is shortened.

The cameras ability to detect a temperature difference across a scene is very essential

in many situations, it is therefore necessary to realize how temperature sensitive the

camera is during decreased exposure time. For a 10°C difference, the camera was having

a difficult time observing any temperature difference between the two blackbodies when

the noise level is equal to the detected signal which is appearing at an exposure time

shorter then 4 ms, figure 4.4 display SNR values smaller then 1, this is due to the large

uncertainty because of small values with large fluctuations. Having a higher temperature

difference will indeed mean that the temperature sensitivity limit will not be reached as

fast during decreased exposure time, but the use of a smaller temperature difference will

replicate a real life scenario of the blend in effect already mentioned above.

The used of a rotating blade effects two parameters: the exposure time and the time

between each exposure (the signal decay time). A ghosting effect where multiple repeats

of a moving object appear during too short exposure time can be avoided by operating

while keeping a long enough signal decay time so that the camera can ”keep up” with

the quick changes. Figure 4.5(d) show no ghosting effect, the allowed signal decay time

for this case is approximately 45 ms because the rotating chopper blade is divided into

10 equal sections. Operating at an exposure time of 1 ms means a signal decay time of

only 10 ms leading to the signal memory behaviour, there is also no need to run at such

high chopping speed since image quality is very poor. Another approach to prevent the

signal memory would be to use a more conventional equipment as an high speed optical

shutter.

CHAPTER 5

Future Work and Conclusion

5.1 Future work

The objective of this work was to investigate if high speed shutter technology can reduce

the motion blur effect in a microbolometer. Since it is now confirmed to be an reasonable

solution, the next step would be to invest in an optical shutter and implement it with the

camera to create a single operational unit to be used for real life situation as capturing

images of objects travelling at high speed while still maintaining an acceptable image

quality.

5.2 Conclusion

The challenging questions of this work was answered by organising a set of equipment

with an microbolometer detector camera to test if motion blur can be reduced with the

help of high speed shutter technology. Equipment used to modify the exposure time is a

modified optical chopper usually used to modify light to pulsated form. The behaviour of

the signal detected by the camera during modified exposure time was also investigated to

provide a deeper understanding of the microbolometer operation, two blackbody radiators

was therefore used. The software controlling the camera was used to collect the important

data to be analysed.

The detected object signal behaviour was investigated initially, it was concluded that

the step response for the microbolometer follow theoretical behaviour of a response of first

order, this was confirmed with plots. Using the plots, the time constant for the camera

was also determined to be able to compare it with provided value from the manufacturer.

How temperature sensitive the camera is with reduced exposure time was also studied.

A visualising plot was provided with a conclusion that important temperature information

was lost with reduced exposure time making it critical to set a boundary for how short

the exposure time is allowed to be dependent of how temperature variant a viewed scene

27

28

is.

The motion blur effect appear when the camera is viewing an object moving at high

speed. This was investigated by dropping a steel ball and capturing video sequences for

a reduced exposure time. It was concluded that the motion blur effect is significantly

reduced with shorter exposure time while still maintaining an acceptable amount of

image quality. For very short exposure times, a ”signal memory”, displayed as a ghosting

effect of the moving object, is visible beacuse of the slow operation of the camera. It

is important to set a boundary for how short the exposure time should be to reduce

the motion blur effect while still maintaining important thermal information and image

quality.

Finally, Thermal imaging technology is widely used in many applications as mentioned

in chapter 1 and this work increased the possibility to facilitate the use of microbolometer

technology in applications were high speed imaging are essential. There is a possibility

to integrate a microbolometer camera with an implemented high speed shutter in the

infrared imaging system for a fighter jet or a helicopter to identify objects with high

precision and with small amount of motion blur.

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