This question is about image-processing methods to emulate the noise reduction and increase in details achieved by cameras with good built-in pixel shift (Sony A7 RIII, Pentax K-1 and others). I am assuming situations where pixel shift works well, particularly a good tripod, no wind, a static subject and enough time to take a few pictures.
There are several methods that I can think of, but none is really satisfactory:
- Taking 4 identical images with a remote trigger and averaging. This effectively reduces the noise but doesn't bring up any additional detail in well exposed areas.
- Adding minimal motion between the shots (e.g. touching the camera) and using superresolution. This eventually brings more detail - in addition to reducing noise - but it does require a lot more than 4 images and the process is really cumbersome.
- Using a lens with 1.75x the focal length and doing a 2x2 panorama with 33% overlap. This requires just 4 images and the process is very straightforward. However, the depth of field shrinks and the aperture would need to be reduced by 3 stops to compensate - otherwise, focus stacking is required, making it as cumbersome as superresolution.
Are there any methods that would be effective and simple? Is there any software that would make superresolution a lot less cumbersome and more effective (e.g. something that would work directly on the RGB channels in the raw files and wouldn't need the intermediary expanded TIF images)?