Online Training on Color Pipeline of a Camera

Framos announces an "Online Training: Colour Pipeline of a Camera" by be delivered by Albert Thuwissen on July 6-7, 2020.

The training will start with a short overview of the sensor and the lens, and will then dive into the details of a “standard” colour pipeline that is used to make a colour image out of the raw sensor signal. The following topics will be discussed:

• Auto 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!
• 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?
• 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 coefficients?
• 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.
• 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 each other.
• 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 temperature effects?
• 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?
• Defect Correction: How can defect pixels be corrected without any visible effect? Can similar techniques also be applied to correct defect columns?

Although not really part of the colour pipeline, the following aspects of a digital camera will be discussed in the training as well:

• Auto-exposure: How can the data of the image sensor itself being used to optimize the exposure time of the imager?
• Auto-focusing: How can the data of the image sensor itself being used to activate the auto-focusing function?