State of the art in upsizing algorithms?

Started Jun 9, 2014 | Discussions thread
Mark Scott Abeln
OP Mark Scott Abeln Forum Pro • Posts: 15,716
Re: State of the art in upsizing algorithms?
1

hjulenissen wrote:

The baseline for image scaling is working in some desirable domain (gamma vs linear, colour space, ...) and choosing the appropriate linear filter (some time vs frequency trade-off, for instance lanczos2/3). For some applications you might want to do post sharpening tightly integrated with the scaling operation. The ImageMagick link that you found is an excellent resource.

Dr. Robidoux, the author of the paper, gave me the link himself.

One striking finding in the link is that while downsampling ought to be done in a linear space, up sampling instead should be done in a gamma space or another space that is gamma-like such as Lab.

Now I’m not quite sure how this finding ought to be applied — that demosiacing ought to be done after gamma correction, if I am ultimately upsizing an image?

From my reading and experimentation, it seems that demosaicing ought to be done rather late in the image processing pipeline. One manual for dcraw makes the case that it ought to be done after white balance, and for the same reasons it seems prudent to do it after color correction. But after gamma also?

One technique that I found very useful, back in 2008 with Photoshop CS3, was using ACR to produce a  larger image — I used about 11 MP — from my 6 MP camera images I got from my old D40. I found that I could do a lot of geometric image transformation, including perspective distortion, barrel distortion removal, defringing, etc. without losing too much detail or having too much additional softening, and the perception of sharpness could be restored with good downsampling algorithms and sharpening. Note that this was a terribly slow ordeal on my old iMac with 1 GB memory but well worth it.

The "better than linear filtering" commercial applications that are available seems to often rely on some kind of edge-adaptive algorithm, meaning that sharp edges (e.g. text) can be blown up sharp and smooth.

I think that there are some applications of "non-local" methods for denoising/upscaling that might benefit certain kinds of images (i.e. exploiting image self-similarity).

My photograph is of a large, historic, top-quality mosaic, and so has lots of brightly colored triangles and trapezoids separated by thin lines of mortar. This is a subject that could really benefit from an algorithm that detects and preserves edges.

There is that commercial package (Perfect Resize 8) which upsizes using fractal decomposition. I’ll see if this might work well also.

Another direction I’ve been thinking of is using software to create a Scalable Vector Graphics or SVG representation of my image — IF it can detect the edges well enough and crisply enough, then I can blend the perfectly resized vector graphics with the upsampled image. Seems plausible, but I don’t know if anyone’s tried this or what software might work best.

For my own humble needs (18MP APS-C, A2 printer), I have found that the sensel grid is seldom a significant limitation (but lens quality/focus can be).

If I had known that my photograph would be purchased, I’d have done a more heroic effort in my photography.  But as it happens, I did use good technique (heavy tripod, manual live view focus, mirror lockup and remote shutter release) and a good lens, because I did want a quality final image for my own use. 
My concern is that the final image will be printed at 100 DPI or maybe less (they haven’t yet told me the final dimensions). Certainly I’ve had good success with even lower resolution large prints, albeit heavily processed — and most civilians aren’t pixel peepers — but I do want even better results this time.

 Mark Scott Abeln's gear list:Mark Scott Abeln's gear list
Nikon D200 Nikon D7000 Nikon D750 Nikon AF-S DX Nikkor 35mm F1.8G Nikon AF Nikkor 50mm f/1.8D +2 more
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