State of the art in upsizing algorithms?

Started Jun 9, 2014 | Discussions thread
hjulenissen Senior Member • Posts: 2,268
Re: State of the art in upsizing algorithms?

The_Suede wrote:

The Bayer pattern is the most densely populated sparsity root possible in the chain, so it (or rather; the mathematically perfect re-modelling of the underlying material in it) defines the maximum possible resolution you can hope to achieve.

If you (successfully) model the input scene, and for some definitions of "resolution", I guess that there are few limits to the output resolution that can be had.

You don't actually GAIN anything by trying to merge the re-population of the sparse samples and the scaling, since your limitation is in the original sparse pattern.

If scaling and deconvolution performs worse than expected due to the nature of a demosaiced signal, then there might be potential gains to be had from making them aware of this process (or integrating the whole thing)?

What you could gain is maybe some computational efficiency

It seems that computational efficiency is not what the OP had in mind. I am guessing that the commercial parties have optimized their 16-bit fixed-point (?) SIMD implementations quite well for x86 and a given, moderately good, quality level?


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