Hi
@Masahiko,
I assume that you measure the calibrated/debayered data, not the registered images. Measuring frames before registration is a more precise approach in terms of extracting image properties like FWHM or noise since the images are not yet affected by the interpolation introduced by the registration process. This way, the measured information is representative of your original data and is still meaningful if you want to perform drizzle integration later.
To first compare performing a manual rejection by blinking and using SS or letting WBPP perform the whole job to produce the master light, you have to understand how the weighting method works and if you really want to put in place a weighting/selection criteria that make a real difference and/or bring concrete benefits to the result.
The weighting methods are quite powerful in terms of producing extremely good results by properly handling good and bad frames, and you have more than one method depending on the objective you want to achieve, like maximizing SNR or balancing both SNR and star quality for an overall good result.
The initial blinking may be useful to discard really bad frames with poor tracking or heavy clouds. Blinking is always a good practice I think everyone puts in place to give a look at the data.
Performing a manual selection of the frames using whatever criteria may be useful to save execution time, but I would claim that it won't produce significantly better (if not worse) results unless you aim for some different objective than the ones mentioned above. The weighting methods already assign a low weight to "bad" frames; the key point is what "bad" means in terms of the quality objective you have in mind.
What you may detect as a bad frame may still include some signal that is worth including. If you have a faint object, it may be even worse to remove them instead of simply assigning a low weight. There is also the consideration that if you have the minority of frames with poor tracking, then they should not ruin the shape of the star since the worse frame's contribution should be rejected. Finally, you have the option of setting a minimum weight level for the images to be integrated; with this parameter (under Light settings in WBPP), you will not integrate the frames with too low weight values.
Finally, with the arrival of the new WBPP 2.5.0, that filtering operation will be performed by WBPP itself.
So, resuming, there is nothing really against measuring and evaluating your data; the point is how much effective you could be by simply removing "bad" frames with respect to what the weighting systems already do to get a better SNR or to get an overall better image.
I think that "removing bad frames" is an old-school wrong way to do it
