Here is a side-by-side comparison to show the typical performance of the new large-scale pixel rejection algorithm:
(http://forum-images.pixinsight.com/20170512/ls_rejection_comparison-tn.jpg) (http://forum-images.pixinsight.com/20170512/ls_rejection_comparison.jpg)
Click on the image for a full size version (http://forum-images.pixinsight.com/20170512/ls_rejection_comparison.jpg)
On the left, the result of a normal integration of 10 calibrated, demosaiced and registered DSLR raw frames, using Winsorized sigma clipping rejection with clipping points set to 5 and 4 sigmas, respectively for low and high clipping. On the right, the same integration with the new large-scale pixel rejection algorithm enabled. The plane trail has been completely rejected. It can be rejected equally well even if the high clipping point is raised up to 5 sigmas in this example, which I have selected because I think it's quite representative of this kind of problems in typical data sets.
With large-scale pixel rejection, now we can overcome virtually all problems caused by large and bright spurious objects with high uncertainty borders, such as plane and satellite trails and flashes, meteors, stars on sky flat frames, RBI artifacts, etc. As Vicent has shown, integrated comet images can also benefit from a much better rejection of trailed stars. The algorithm works the same way for high and low outlier structures, so it can also reject large dark artifacts as well. Since large-scale rejection works in a completely automatic way (basically, you only have to enable it), getting the most out of your data sets can now be easier than ever.