Hi,
It is likely this has been shown before- but based on the response I received at AIC when I demonstrated this technique, it may be deserving of attention here (perhaps it will prove useful to someone). This demonstrates selectively rejecting problems in images that would otherwise not be rejected by statistical means. See below my pictures for a few comments. The basic steps are:
1. Identify problems in a set of images. The problem to be rejected should be a small subset of the total number of measurements/images made.
2. Create a black image of zeros (a paint bucket) and using the CloneStamp tool paint zeros on problems. Generally do this to image before registration if the problem is in many images, see below.
3. In the case shown below, the problem were dust donuts not flatted out properly. They exist in precisely the same spot for a given subset (night) of data...
4. If applying the painted area to many images, create a CloneStamp process and then apply to many images using an ImageContainer.
5. Register images in the normal way. Nearest Neighbor may give better results.
6. In ImageIntegration set up pixel rejection in the normal way. Make certain the Range Low is set to "0" and Reject Low Large Scale Structures is checked.
Comments: Obviously the S/N in rejected areas will be different- however the price may be small if the area isn't "important" or if the area was already a high signal (bright) region. I have not experimented with the effects of registration. I suspect all will work out well even with smoothing due to interpolation. However, performing this step on data after registering images will certainly work. With a large dither- the problem areas may dance around a bit.
In the last image, I show the difference of with and without the large scale rejection which takes care of the boundaries very well and makes selective rejection a reality.
Thanks.
-Adam Block