SAMPLE SCREEN SHOTS

MAIN INTERFACE

CCDStack's easy to use interface gives you all the controls you need to quickly
process your images with an intuitive workflow.

 

 

IMAGE MANAGER

The Image Manager is at the heart of CCDStack's power. Load all you images in
a stack and perform operations to all your images at once.

 

 

DATA REJECTION

CCDStack's powerful data rejection system allows you to quickly remove artifacts
from your stack of images. You are not restricted to one form of rejection. For instance,
You can remove hot and cold pixels, reject blooms and perform a STD sigma reject on
the stack. Once completed, you can then combine the stack using sum, mean or median.

 

 

IMAGE REGISTRATION

Registration of multiple files just got a whole lot easier. Star Match, new in Version 2, provides automatic alignment based on star matching. It requires similar image scale (e.g. can accommodate very small adjustments due to filters and such). provides precise registration automatically. Star Snap uses star centroids to calculate the necessary transform(s). FFT is an automatic registration that does not rely on stars. Manual alignmentfprovides full manual control over shift, rotation, and magnification. CCDIS is a robust star pattern alignment that ccommodates different image scales. Choices of resampling algorithms have been increased to give you the best choice for your data and system parameters.

 

 

REAL-TIME DDP SCALING, SHARPENING AND LEVEL ADJUSTMENTS

By separating the underlying 32-bit image data from the display bitmap shown on the screen.
CCDStack allows you to adjust DDP scaling, sharpening and level adjustments in real-time
without making changes to the underlying data. This means that you are free to experiment
with multiple combinations without permanently corrupting the original image data.

 

 

AUTOMATIC DECONVOLUTION SETUP

Version 2 automatically selects multiple, isolated stars to get a more representative point-spread function (PSF), that is critical to successful deconvolution. This feature, together with performance tuning, allows frequent iteration to achieve the level of deconvolution you want.