Changes between Version 147 and Version 148 of ppStack_testing_201111

Timestamp:

Jun 28, 2012, 3:44:29 PM (14 years ago)

Author:

Mark Huber

Comment:

--

ppStack_testing_201111

@@ -748 +748 @@
 
 Want to explore not necessarily setting the convolved "matched/equilized/homogenized" deep-stack target PSF to what generally appears to be driven by the worst input. Have seen cases when the poor inputs could be image rejected and in that case not a good idea to use anyways, and the target PSF re-chosen and stack remade. How can poor imputs be pre-filtered better? Is a most common input based target PSF better? What level of deconvolution is acceptable? 
-
- * for large input image N, a rerun of PSF evelope/target PSF would be of little time cost but rerun of all the image convolutions is terribly expensive. Will want better pre-rejection at least, however, addition of a deconvolved image may be acceptable.
- * for small input image N, may want to reject anyways? Adding poorest image versus dropping 20-30% of possible inputs?
- * instead of a rerun of target PSF with additional image rejection, could choose mean/median PSF or an optimal and use poor deconvolution to reject worse images?
- * rejecting inputs for convolved stacks is the same for unconvolved stacks (for the pixel rejection method), so excessive rejection not good for either.
- * expect the rejecting and target to be different for MD (similar good inputs, easy to clip worse seeing tail without significant loss of depth) versus LAP (similar to wildly variable quality inputs, maybe few far outliers or maybe even spread of quality levels).
+ * simple cuts: 
+  * for large input N, a rerun of PSF evelope/target PSF would be of little time cost but rerun of all the image convolutions is terribly expensive. 
+  * for small input N, enter into decision of adding poorer images versus dropping 20-30% of possible inputs.
+ * instead of a re-run of target PSF with additional image rejection, could choose mean/median PSF or an optimal and use poor deconvolution to reject worse images?
+ * important to remember, rejecting inputs for convolved stacks is the same for unconvolved stacks (for the pixel rejection method), so excessive rejection not good for either.
+ * expect the rejecting and target PSF cases to possibly be different for MD (similar good inputs, easy to clip worse seeing tail without significant loss of depth) versus LAP (similar to wildly variable quality inputs, maybe few far outliers or maybe even spread of quality levels).
 
 Purpose of convolved stacks?
@@ -761 +761 @@
 
 Test cases:
- 0. Greatly reduce the input image FWHM limits and accept possibly fewer inputs into the stacks. For stacks that fail without enough inputs, have a second pass with more relaxed limits. Pass 1 could also require a larger number of minimum inputs (larger than the normal 2-6 inputs currently) with a very restrictive "FWHM" cut (good). Pass 2 more relaxed allowing fewer minimum inputs and/or relaxed cuts (good as going to get).
+ * Case 0: Greatly reduce the input image FWHM limits and accept possibly fewer inputs into the stacks. For stacks that fail without enough inputs, have a second pass with more relaxed limits. Pass 1 could also require a larger number of minimum inputs (larger than the normal 2-6 inputs currently) with a very restrictive "FWHM" cut (good). Pass 2 more relaxed allowing fewer minimum inputs and/or relaxed cuts (good as going to get).
   * affects the unconvolved and well as the convolved however
- 1. Enhance the already exising simple model target PSF code to interally use the already calculated input FWHM mean (and stdev) to set a simple target PSF.
+ * Case 1: Enhance the already exising simple model target PSF code to interally use the already calculated input FWHM mean (and stdev) to set a simple target PSF.
    * is a convolution to a simple model PSF workable
    * how much deconvolution is acceptable in the inputs to the stack
- 2. Either modify the PSF envelope code to trend towards the more likely target PSF or sub-select the inputs for the the target PSF to be set to.
+ * Case 2: Either modify the PSF envelope code to trend towards the more likely target PSF or sub-select the inputs for the the target PSF to be set to.
    * open question if the PSF envelope code is behaving as intendent and needs to be tested further
    * then similar to case 1 deconvolution acceptable over the field