I'm surprised the author is unfamiliar with Google Camera and its super-resolution features[1,2], which uses actually clever algorithms to push digital photography beyond what would be physically possible to get out of a naive set of HDR exposures, both in terms of resolution and dynamic range.
The author spends a whole paragraph talking about this category of techniques:
> Slightly more objectionable, but still mostly reasonable, examples of computational photography are those which try to make more creative use of available information. For example, by stitching together multiple dark images to try to make a brighter one. (Dedicated cameras tend to have better-quality but conceptually similar options like long exposures with physical IS.) However, we are starting to introduce the core sin of modern computational photography: imposing a prior on the image contents. In particular, when we do something like stitch multiple images together, we are making an assumption: the contents of the image have moved only in a predictable way in between frames. If you’re taking a picture of a dark subject that is also moving multiple pixels per frame, the camera can’t just straightforwardly stitch the photos together - it has to either make some assumptions about what the subject is doing, or accept a blurry image.
Their point is that it's not magic; these techniques rely on assumptions about the subject being photographed. As soon as those assumptions no longer hold, you start getting weird outputs.
I have used 3 pixels and never seen any of this bad post processing as shown by author. I never thought iPhones camera could do bad post processing like this.
Seen this in cheap point and shoot cameras and cheap chinese phones though.
> Slightly more objectionable, but still mostly reasonable, examples of computational photography are those which try to make more creative use of available information. For example, by stitching together multiple dark images to try to make a brighter one. (Dedicated cameras tend to have better-quality but conceptually similar options like long exposures with physical IS.) However, we are starting to introduce the core sin of modern computational photography: imposing a prior on the image contents. In particular, when we do something like stitch multiple images together, we are making an assumption: the contents of the image have moved only in a predictable way in between frames. If you’re taking a picture of a dark subject that is also moving multiple pixels per frame, the camera can’t just straightforwardly stitch the photos together - it has to either make some assumptions about what the subject is doing, or accept a blurry image.
Their point is that it's not magic; these techniques rely on assumptions about the subject being photographed. As soon as those assumptions no longer hold, you start getting weird outputs.