Lynkeos uses some signal processing concepts which are explained hereunder.
The image recorded by the detector is not perfect (otherwise you wouldn't
use this software). It can be described as a perfect image deteriorated
by some processes (turbulence for example).
In signal processing, the perfect image is called the "signal" and the
deteriorations, the "noise".
The term noise usually refer to a statistically random deterioration,
for a systematic one we use the term "bias".
When many images are added, the signal and the biases are added (they
grow linearly with the number of images), while the noises are added with
a quadratic sum. This means that the resulting noise is the square root of
the sum of the squares of the noise in each image (remember that
Pythagore theorem?).
For short, when stacking, the signal and the bias grow much faster than
the random noise : stack 4 images, the signal and bias are multiplied by
4, the random noise by 2, the signal is two times stronger than the
random noise in the resulting image. As the biases accumulate with the
signal, they shall be corrected before stacking.
The identified noises/biases, our images suffers from are :
Many functionalities of this software each intend to reduce the amount of one kind of noise.
Prior to any processing, the source image must be calibrated,
ie to remove the biases.
To create the calibration frames described hereafter, all the webcam
settings shall be (except otherwise noted) the same as for the recording
of the images to be processed
The dark frame is a stack of many frames recorded with the telescope
shut, at the same temperature as for the recording of the images to be
processed. It is a measure of the thermal bias.
It is substracted from each image before processing.
The flat field is a stack of many images recorded with the telescope
looking at an evenly illuminated target, with the same optical setup as
the images to be processed, at a shutter speed giving a bright enough, but
non saturated image. It is a measure of the uneven
transmission/sensitivity.
Each image is divided by the flat field before processing.
To align two images, Lynkeos uses intercorrelation.
To speed up the computing, it uses a fast fourier transform (FFT), this is
why the alignment box is a square with a power of two as side.
It then searches a peak in the correlation result, which gives the
alignment offset between the images.
Some user preferences affect this processing :
To compute an image quality level, Lynkeos uses one of two methods, based on :
To stack the images, Lynkeos translate the images by their alignment offset down to fractions of pixels. Each pixels is split in 4 according to the pixel fraction and accumulated in the pixels of the resulting image.
The stacking is done with floating numbers, there is no risk of "brightness overflow" if you have many bright images.
The final processing uses two filters, the deconvolution filter and the unsharp masking filter
The deconvolution filter is exactly what it says (a Wiener deconvolution
filter to be precise). This is done by dividing the spectrum of the image
by the spectrum of the presumed convolver, a gaussian in this case.
The threshold value is the convolver spectrum module value under which the
division is not performed. This avoids amplifying too much the highest
frequencies (experiment with low threshold to see what I mean).
The unsharp masking filter is the digital translation of a darkroom technique. It substracts a blurred image to the original image and amplifies it. This amplifies small scale details and attenuate large scale brightness fluctuations.
The filtered image, wich is a floating numbers image is then
brought back to an 8 bit integer RGB image for display on the screen. This
conversion uses the black and white levels provided by you.
There is once again no risk of brightness under or overflow whatever
processing you do (don't forget to adjust the levels).
The filtered image is converted the same way to a 16 bit integer RGB image for exporting into a TIFF file