Digital Camera Systems
November 7 - 9, 2016
. Barcelona, Spain
We recommend you to submit your
preliminary or firm registration at least 4 weeks before course
start to ensure a seat on the course.
PLEASE OBSERVE: The course runs
Tuesday-Thursday in June 2016
and Monday-Wednesday in November 2016
Digital cameras are an essential part of our daily life, e.g. in
mobile phones, camcorders, digital photography, cars, and in
imaging applications for medical, industrial and broadcasting
industries. All these camera applications rely on the solid-state
However, if consumers were forced to choose a digital camera on
the basis of the raw data produced by the imager, it would be very
doubtful that anyone of us would buy a digital camera. The data
produced by a solid-state image sensor is contaminated by various
noise sources, by defects, by inconsistencies, and many other error
sources. To make matters worse, the solid-state image sensors do
not themselves produce a coloured image. It is the data processing
that must correct all these potential errors and even regenerate
the colour information in the post-processing stage. So, what a
person actually sees on a display or hard copy is absolutely not
the same as what the imager has captured. Luckily - "What you see
is not what you got!"
As we can foresee that our homes, offices and cars soon will be
fully equipped with cameras to make life safer and more enjoyable
and to reduce our workload, we can recognize the digital camera
technique as a forefront technology. Even today, for many
applications imaging is in the embryo stage of its development.
COURSE CONTENT AND OBJECTIVES
This course will focus on the overall system aspects of digital
cameras. The complete path from "photons-in" to
"digital-numbers-out" will be discussed. The effect of light
sources, optics, imagers, defects, and data processing will be
covered. Computer animations and simulations will be used to
achieve a realistic understanding of details and shortcomings in
digital cameras. Many examples of images will be used to explain
the various issues. No detailed knowledge of device physics is
assumed. This course is complementary and completely different from
Courses #13 Digital Imaging: Image Capturing,
Image Sensors - Technologies and Applications and
Advanced Course on Image Sensor Technology.
TUESDAY June 14 / Monday November 7,
The Image Sensor, the Optics, Image Processing: A
The course begins with a brief overview of the basic
theory of solid-state image sensors. The imager is of course only a
small, but vital, component of the complete camera system. The
effect of the spectral content of the light sources will be
discussed. Lectures on optics and on digital image processing are
included to form a strong backbone for the remaining parts of the
Noise, defects, irregularities of the video signal, and
inconsistencies can all deteriorate the quality of the image. We
will discuss where these problems come from and how they can be
corrected. The dilemma that correcting one effect can have a
negative impact on some other camera parameters will be
WEDNESDAY June 15 / Tuesday
November 8, 2016
Digital Camera Systems
- Dark Current Compensation: The average value
of the dark current can be corrected by the use of dark-reference
lines/pixels. Fixed-pattern noise can be corrected by means of dark
frame subtraction. How efficient are these techniques? What is
their influence on signal-to-noise performance and what about
- Colour Interpolation: The Bayer pattern
sampling is extensively used in digital imaging, but the sampling
is only half of the story. The other half is the demosaicing or
interpolation. Several methods will be discussed and compared with
- White Balancing: The human eye is adapting
easily and quickly to the spectrum of a light source, the image
sensors do not adapt at all! How can we deal with this
"shortcoming" of the imagers?
- Defect Correction: How can defect pixels be
corrected without any visible effect? Can similar techniques also
be applied to correct defect columns?
- Noise Filtering: A very important issue in
data processing is the filtering of any remaining noise. This can
be done in a non-adaptive or an adaptive way. What are the pros and
cons of the various techniques?
THURSDAY June 16/ Wednesday November 9,
Digital Camera Systems (cont´d)
- Colour Matrixing: Nobody is perfect, neither
are the imagers that suffer from optical cross-talk and from
imperfections when it comes to the transmission characteristics of
the colour filters. Colour matrixing takes care about these issues.
Question is how to find to optimum correction matrix
- Contouring: This is a technique to "regain"
details, edges and sharpness in an image. But quite often not only
the details are enhanced, but the noise in the image as well.
Various contouring techniques will be discusses and compared with
- Lens-Vignetting: Lenses have a strong fall-off
of intensity and sharpness towards the edges. On top of that, also
the image sensor will add an extra fall-off of intensity. Is
correction possible? How complicated needs the correction to be to
become invisible for the observer?
- Auto-focusing: How can the data of the image
sensor itself being used to activate the auto-focusing
- Auto-exposure: How can the data of the image
sensor itself being used to optimize the exposure time of the
- Gamma Correction: How to adapt the brightness
of the output device to the characteristics of the human eye?
Said about the
course from previous participants:
Good examples and illustrations. Deep knowledge of
the topic. Very good teacher and material gave excellent
overview to imaging area. Not too specific details so easy to
follow. Good examples, also theory behind these were explained
well. Would recommend this course to
my colleagues. Step-by-step process through the whole chain.
Good explanations of each step."