Hi all,
It's been pretty quiet here lately, so just wanted to warm things up a little bit. We have a new tool almost ready for the next 1.8.5 release of PixInsight: FrameAdaptation. This tool performs a local normalization of images based on multiscale robust statistics analysis. Here is a comparison:
Before FrameAdaptation:
(http://forum-images.pixinsight.com/20170511/FA/FA-before-tn.jpg) (http://forum-images.pixinsight.com/20170511/FA/FA-before.jpg)
Click on the image for a full size version (http://forum-images.pixinsight.com/20170511/FA/FA-before.jpg)
After FrameAdaptation:
(http://forum-images.pixinsight.com/20170511/FA/FA-after-tn.jpg) (http://forum-images.pixinsight.com/20170511/FA/FA-after.jpg)
Click on the image for a full size version (http://forum-images.pixinsight.com/20170511/FA/FA-after.jpg)
What you see here are screenshots of my workstation with a working/testing version of the tool. The final version will be very different. It will work in batch mode just like StarAlignment, and will have a few more parameters. In this example, the reference image is on the left. On the right we have a different image of the same data set, with much less signal and a very strong gradient. Obviously, both images have been previously calibrated and registered.
After FrameAdaptation, background and signal levels have been normalized on a pixel-per-pixel basis. The gradient is completely gone as a result of this local normalization process. FrameAdaptation will be an optional image preprocessing step between registration and integration. It has other interesting applications, such as color correction and automatic gradient modeling/correction, among others. More or less, you can think of FrameAdaptation as a local version of LinearFit, able to work at different dimensional scales.
More warm-ups coming soon, so stay tuned. The next one will probably be a new mosaic blending tool based on... let's keep this secret for now :) Just two words to describe it: *seamless* mosaics. I'm working on it.
Hi Ron,
This tool will be named MultiscaleLocalNormalization (MLN) instead of FrameAdaptation, since MLN describes much better what it does and how it works. No, MLN won't be part of BPP, since BPP must not be used for image integration of light frames. MLN will be available in the ImageIntegration tool as a new pixel rejection normalization algorithm. Thanks to MLN, pixel rejection will work independently of gradients and other local signal variations, which leads to important SNR improvements. Here is an example:
(http://forum-images.pixinsight.com/20170525/MLN/MLNR-comparison-tn.png) (http://forum-images.pixinsight.com/20170525/MLN/MLNR-comparison.png)
Click on the image for a full size version (http://forum-images.pixinsight.com/20170525/MLN/MLNR-comparison.png)
In this example we have integrated a data set with strong signal variations and gradients caused by varying atmospheric conditions. With the usual scale + zero offset rejection normalization, pixel rejection (Winsorized sigma clipping in this case) makes many mistakes because local illumination variations cannot be modeled correctly with a global image normalization. These mistakes lead to many outlier pixels surviving the rejection phase, as can be seen on the left of the above screenshot. With the MLN algorithm (on the right, applied at the scale of 256 pixels in this case), pixel rejection is virtually perfect even in this difficult case. In this example, MLN has also been applied as an output normalization for integration.
MLN, along with the new large-scale pixel rejection algorithm, are an important step forward in our image preprocessing tool chain.