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They aren't. They compare to techniques "A+" from R. Timofte, V. De Smet, and L. Van Gool, “A+: Adjusted anchored neighborhood regression for fast super-resolut
by lotyrin 10y ago
They aren't. They compare to techniques "A+" from R. Timofte, V. De Smet, and L. Van Gool, “A+: Adjusted anchored neighborhood regression for fast super-resolution" http://www.vision.ee.ethz.ch/~timofter/ACCV2014_ID820_SUPPLEMENTARY/ http://www.vision.ee.ethz.ch/~timofter/ACCV2014_ID820_SUPPLE...
and "SRCNN" C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution” https://arxiv.org/abs/1501.00092 https://arxiv.org/abs/1501.00092
Their result seems not to score as well in quantitative metrics (signal to noise ratio), but is faster. (And seems to have good qualitative results, in my opinion.)
- amelius 10y agoIndeed, I just skimmed through the paper, and the results don't seem much better than A+ and SRCNN (even worse in some instances).
- ska 10y agoYes, they aren't notably better, but they are a lot faster. Maybe useful. I really wish people wouldn't call these algorithms super-resolution, as they already had a useful meaning.
- TTPrograms 10y agoI think superoscillation is a better description of the phenomena in E&M/optics.
- romaniv 10y agoI'm speaking about the pictures on the website OP links to. They even say "As you can see in the example below, the upsampled image looks blurry – one would hesitate to call it enhanced." It's blurry, because they use an algorithm that produces blurry images. Lanczos, for example, would produce a much sharper picture at the expense of some minor artefacts. They don't show A+ results anywhere in the blog-post at all.