Anna Fabijańska

Dominik Sankowski

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Instrumentation and Measurement 
Technology Conference – IMTC 2007 
Warsaw, Poland, May 1-3, 2007 
CCD Camera Instrumental Background 
Estimation Algorithm 
Dominik Sankowski 
Anna Fabijańska 
Computer Engineering Department, Technical University of Lodz 
18/22 Stefanowskiego Str, 90-924 Lodz, Poland 
Phone: +48-42-631-2750, Fax: +48-42-631-2755, Email: {dsan, an_fab}@kis.p.lodz.pl 
 
Abstract – Noise can seriously affect quality of digital image. Its 
presence results with compromising a level of details. There are 
numerous methods of noise removal, however in general, they do 
not consider the specificity of CCD camera electronic components. 
In this paper method of CCD camera instrumental background 
removal is presented. Results of applying the algorithm to images 
obtained from image quantitative analysis system are presented and 
discussed. A brief description of the system is given. 
  
 
Keywords – image enhancement, image noise, instrumental 
background, image quantitative analysis system.  
I. INTRODUCTION 
CCD imaging sensors have become very popular 
nowadays. They have numerous advantages over 
photographic films so they are successfully used in many 
scientific applications for example in image quantitative 
analysis systems.  
 The images provided by CCD cameras are characterized 
by high quality, however they are not completely free from 
different types of distortions and artifacts. 
 All factors that produce non-uniform pixel output are 
called noise. Mostly, noise arises from electronic components 
of cameras [1][2]. There are three primary components of 
noise in CCD imaging systems: photon noise, readout noise 
and dark noise [1][2][5][6]. 
 Photon noise is a fundamental feature of the quantum 
nature of light and is irreducible due to poissonian nature of 
counting photons. The second noise component – readout 
noise (known also as preamplifier noise) is generated by the 
on-chip output amplifier and cannot be completely removed 
from image. Finally dark noise (known also as dark current) 
is thermally generated charge that can be approximately 
measured and subtracted form the output image. 
Noise can seriously damage digital image quality. Its 
presence compromise a level of details and in consequence 
makes important image information lost. In order to restore 
original image, noise removal process should be carried out.  
 There are numerous methods of noise removal 
[12][13][14]. In general they can be divided into two main 
groups: image filtration and image averaging. However, 
filtration affects with blurred appearance of an image what is 
unacceptable in case of digital image analysis systems. 
Averaging - the second method of random noise removal is 
said to reduce noise level without compromising details. 
However, it involves at least several images of observed 
scene and in consequence cannot be used when view field 
changes rapidly.  Moreover, in case of real-time processing 
all averaged images have to be stored in image processing 
system memory.  
 In general, custom methods of image reconstruction due 
to noise removal do not consider the specificity of CCD 
camera electronic components. All types of noise are treated 
in the same way. This affects with details compromising. 
 In the following part of this paper, method of CCD 
camera dark noise estimation and removal is proposed. The 
described method iteratively constructs successive 
approximations of image instrumental background.   
 Results of applying authors’ algorithm to images obtained 
from computerized system for high temperature 
measurements of surface properties (wetting angle, surface 
tension) of liquid and solid in contact are presented and 
discussed. A brief description of the system is given. 
II. THE EXPERIMENTAL SETUP 
The algorithm of CCD camera instrumental background 
elimination was tested on images acquired from “THERMO-
WET” - computerized system for high temperature 
measurements of surface properties. Surface properties are 
calculated on the basis of drop shape analysis [8][9][10]. 
The system is property of Computer Engineering 
Department of Technical University of Lodz. 
 The apparatus of “THERMO-WET”consists of: 
1. high-temperature (up to 1800°C) electric furnace with 
a protective atmosphere; 
2. system for the precise temperature measurement and 
control; 
3. technological gases supply system; 
4. specimen insertion mechanism; 
5. vision unit (CCD camera, image analysis and 
processing algorithms, infrared filters with the 
algorithm of their automatic changes); 
6. computer for controlling all stages of the measurement 
process. 
 The general view of the measurement system has been 
presented on Figure 1. More detailed description of 
“THERMO-WET” can be found in [8][9][10]. 
 
 
Fig.1.  The general view of “Thermo-Wet” system; 1- high-temperature 
electric furnace; 2- system for the precise temperature measurement and 
control; 3 - technological gases supply system; 4- specimen insertion 
mechanism; 5- vision unit; 6- computer; 
The measurement process starts from placing specimen of 
investigated material inside the furnace. In order to do so 
insertion mechanism is used. The specimen under 
investigation is then heated to the temperature higher than the 
melting point. When it becomes drop, the “THERMO-WET” 
vision unit acquires images of the specimen and proceeds 
image segmentation. In the next step of the measurement 
process drop geometric parameters are determined (Fig.2) in 
order to calculate surface properties using Potter’s equation 
[10][11]. 
45o
x
h
y
12
A1A2  
Fig.2. The specimen with important geometric parameters marked. 
It should be pointed out that image quality is crucial for 
the measurement process. High quality of input images 
ensures better results of the segmentation process and in 
consequence more accurate determination of drop geometric 
parameters. Because of these facts, segmentation is preceded 
by image enhancement process that is to remove redundant 
information and extract information that is important for the 
measurement process. One of the steps of image 
enhancement is instrumental background elimination. 
III. INSTRUMENTAL BACKGROUND  PRELIMINARIES 
 When no external light flux reaches the CCD sensors, the 
charge is only generated by thermal effects within the silicon 
lattice forming the CCD. Dark frame acquired when the CCD 
camera shutter is closed presents image of instrumental 
background. An example of dark frame is presented in figure 
3a. The corresponding image after histogram stretching is 
also shown (Fig.3b). Presented images were acquired from  
8-bit monochromatic Watec WAT 502A miniature CCD 
camera (which is a part of “THERMO-WET” vision system). 
Fig.3. Example of dark frame; a) original image; b) an effect of histogram 
stretching. 
Instrumental background is present in every digital image. 
It consists of two components i.e. bias voltage and dark 
current in accordance with equation (1). 
 DBDF LLL +=  (1) 
where:   
 
DFL  - image of the instrumental background (dark 
frame); 
BL  - image of bias voltage (zero frame); 
DL  - image of dark current. 
  
 Dark current and bias voltage can seriously affect the 
quality of digital images.  The problem occurs especially in 
case of low intensity images. Figure 4 illustrates the problem 
of image quality degradation due to instrumental background 
presence. Presented images show high-temperature 
specimens of aluminum (Fig. 4a) and copper (Fig. 4b).  The 
specimen temperature is indicated on the figure. Images were 
acquired from “THERMO-WET” vision system using 
infrared filters. Instrumental background presence manifests 
itself by vertical lines of different intensity.  
  
Fig. 4. Image quality degradation due to instrumental background presence. 
 Both, bias and dark current, should be removed from 
output image. The common methods of instrumental 
background components determination require adjustable 
exposition time [3][4][7]. However, this feature is only 
a) b) 
available in astronomical cameras whereas in many scientific 
cameras exposition time is constant [1][2]. 
 The author’s idea of instrumental background 
determination is dedicated to CCD cameras with constant 
exposition time. 
IV. GREY LEVEL DISTRIBUTION ANALYSIS  
 Histogram of dark frame from figure 3a is presented in 
figure 5 and table 1. Only significant grey levels (having 
values higher than 0) are presented. 
 
Table 1. Significant grey levels percentage participation                                       
in the exemplary dark frame 
Grey 
Level 
Percentage participation 
[%] 
0 3.788 
1 18.289 
2 29.434 
3 27.720 
4 13.944 
5 4.907 
6 1.625 
7 0.257 
8 0.034 
9 0.001 
 
 Dark frames analysis led to the conclusion that pixels grey 
levels range is from 0 to 9 in range [0,255].  Zero pixels are 
in minority (less than 4%). Dominant intensities – about 60%, 
are concentrated with values 2 and 3. They are considered as 
result of bias presence. Remaining intensities are images of 
thermally generated charge i.e. dark current.   
 
Fig.5. Significant intensities percentage participation in exemplary dark 
frame from figure 1. 
Experiments proved that dark frame grey levels 
distribution can be considered constant for given CCD 
camera. It means that once constructed master dark frame can 
be simply subtracted from each out coming image.  
The algorithm of master dark frame construction is 
presented in the following section of this paper. Algorithm 
extracts dominant constituents of instrumental background 
using successive iterations. 
 
V. ALGORITHM DESCRIPTION 
The presented algorithm uses a set of dark frames 
acquired from CCD camera. This set is subsequently used to 
construct image of the instrumental background. 
Set of input dark frames is averaged in successive 
iterations in order to extract dominant components of the 
instrumental background. The average frame is then 
subtracted from each of the input frames in order to build 
input set for the next iteration. The process is repeated until 
there are no distortions in the average frame. 
 
START 
Read set of input 
dark frames 
Average input 
frames           
(average frame)    
Subtract average 
from each frame 
in the input set 
Build „zero”  
bitmap 
Add average 
frame to „zero” 
frame  
STOP 
No distortions in  
the average frame       NO
YES 
 
Fig.6. Flow diagram of the iterative dark current estimation algorithm. 
Average frames from consecutive iterations are 
remembered. As it was mentioned before, they present 
images of instrumental background components. In order to 
build master dark frame average frames from all iterations are 
summed up. The algorithm flow diagram is presented in 
figure 6.  
VI. RESULTS AND DISCUSSION 
Image of instrumental background based upon 1800 dark 
frames acquired from Watec WAT 502A miniature CCD 
camera is shown in figure 7. For better results presentation 
effect of histogram stretching is presented.  
 
 
Fig.7. Image of instrumental background based upon 1800 dark frames 
acquired from Watec WAT 502A CCD camera. 
Because dark current is irreducible, it is impossible to 
build master dark frame of infinite accuracy. However, 
accuracy of approximation depends on the number of 
iterations.  
Table 2 presents influence of the number of iterations 
performed on the quality of instrumental background 
approximation. Consecutive images present results of master 
dark frame subtraction from exemplary dark frame. 
First column indicates number of iterations used to 
construct master dark frame. Consecutive images present 
noise left in the exemplary dark frame after instrumental 
background subtraction. For better results presentation effects 
of histogram stretching is shown. 
It can be easily seen that the noise level in the dark frame 
decreases with the number of iterations used to instrumental 
background image construction.  
As it was mentioned before subtracting master dark frame 
from each image results with bias and dark current removal. 
Result of image enhancement due to master dark frame 
subtraction in presented in figure 8. The image presents 
specimen of copper at temperature 8170C. 
 
  
Fig. 8. Result of instrumental background elimination (contrast stretched). 
It can be easily seen that subtracting master dark frame 
improves image quality. Vertical lines of different intensity 
were removed from the analyzed  image. 
 
Table 2. Properties of dark frames obtained in consecutive iterations. 
 Exemplary frame Frame properties 
1 
 
Avg. lightness: L = 2 
Contrast: σ =3 
No-zero pixels: 83,69% 
 
2 
 
Avg. lightness: L = 0 
Contrast: σ = 1 
No-zero pixels: 41,61% 
 
3 
 
Avg. lightness: L = 0 
Contrast: σ = 0 
No-zero pixels: 9,37% 
 
4 
 
Avg. lightness: L = 0 
Contrast: σ = 0 
No-zero pixels: 9,23% 
 
VII. CONCLUSIONS 
 In this paper noise sources in CCD camera images were 
introduced. Particular attention was paid to thermally 
generated charge known as dark noise (or dark current). 
Method of dark noise level estimation for particular CCD 
camera was proposed. The method in successive iterations 
constructs consecutive approximations of instrumental 
background image. Final master dark frame can be then 
subtracted from each image acquired from CCD camera. The 
subtraction improves quality of the output image.  
 Results of applying authors’ algorithm to images acquired 
from industrial quantitative analysis system were presented 
and discussed. 
 Proposed method can be particularly useful in case of low-
light images. However, it can be successfully used in all CCD 
sensors applications as a part of image enhancement process.  
VIII. ACKNOWLEDGMENT 
European Social Fund and Polish State have supported this 
work in the frame of “Mechanizm WIDDOK” programme 
(contract number Z/2.10/II/2.6/04/05/U/2/06). 
 
 
 
IX. REFERENCES 
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Society for Optical Engine 1998. 
[2] J.R Janesick “Scientific Charge-Coupled Devices” SPIE-International 
Society for Optical Engine, 2001. 
[3] S. B. Howell “Handbook of CCD Astronomy”, Cambridge University 
Press, 2006. 
[4] P.Léna, P. Martinez, A.Klotz, A. Demers, “A Practical Guide to CCD 
Astronomy”, Cambridge University Press, 1997. 
[5] http://micro.magnet.fsu.edu/   
[6] http://microscopy.fsu.edu/primer/  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
[7] S. N. Pattanaik, J. Ferwerda, K. E. Torrance, D. P. Greenberg 
“Validation of global illumination simulations through CCD camera 
measurements” Proceedings of IS&T/SID's 5th Color Conference, 
Arizona, pp.17-21 November, 1997. 
[8] D. Sankowski, K. Strzecha, S. Jeżewski, J. Senkara, and                          
W. Łobodziński, “Computerised Device with CCD Camera for 
Measurement of Surface Tension and Wetting Angle in Solid-Liquid 
Systems”, 16th IEEE Instrumentation and Measurement Technology 
Conference IMTC, Venice, Italy, pp. 164-168, 1999. 
[9] D. Sankowski, K. Strzecha, S. Jeżewski, “Digital image analysis in 
measurement of surface tension and wettability angle”, IEEE 
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computer Science and Engineers Training, Lviv, Ukraine, pp. 129-130, 
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[10] D. Sankowski, J. Senkara, K. Strzecha, S. Jeżewski, “Automatic 
investigation of surface phenomena in high temperature solid and liquid 
contacts”. IEEE Instrumentation and Measurement Technology 
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[11] A.W. Adamson, “Physical Chemistry of Surfaces”, Wiley Int. Publ., 
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[12] R. C. Gonzalez, R. E. Woods “Digital Image Processing” Prentice Hall, 
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[13] Ian T. Young, Jan J. Gerbrands, Lucas J. van Vliet  „Fundamentals of 
Image Processing” The Netherlands at   the  Delft University of 
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[14] A.Erhardt-Ferron, “Theory and Applications of Digital Image 
Processing” University of Applied Sciences Offenburg 2000. 
 
 
 


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