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waifu2x (http://waifu2x.udp.jp/ http://waifu2x.udp.jp/) is another often-posted superresolution approach, focusing on upscaling low-res anime images instead of

by blt 9y ago

waifu2x (http://waifu2x.udp.jp/ http://waifu2x.udp.jp/) is another often-posted superresolution approach, focusing on upscaling low-res anime images instead of extremely tiny pixel art. It uses a totally different learning-based approach - however, their approach would not work with pixel art because it depends on having a set of high resolution ground truth images, and downsampling them to generate training data pairs. No such high-resolution examples exist for pixel art.

anonymfus 9y ago
> No such high-resolution examples exist for pixel art. How about application icons?
- blt 9y ago
  good point!
murukesh_s 9y ago
waifu2x seems to be based on Deep Convolutional Neural Networks, while the article looks like a pure handwritten algorithm and at least in this case the algorithm looks like a winner.. I am wondering whether in the future we may forget about writing algorithms altogether and instead rely on ANNs to do tasks that could have done better using hand written algorithms?
- blt 9y ago
  You mean tasks like computing the rank of a matrix? (http://oneweirdkerneltrick.com/rank.pdf http://oneweirdkerneltrick.com/rank.pdf) ;) Image super-resolution is an ill-posed inverse problem: many possible high-resolution images would be reduced to the same low-resolution image. However, some of the possibilities are more realistic than others. Consider a grey pixel in a photograph from real life. It's more likely that it was downsampled from a 2x2 block of grey pixels than from a 2x2 black-and-white checkerboard pattern. We apply such knowledge by adding regularization to the problem, or using a prior distribution in a Bayesian formulation. Deep learning is very good at memorizing those priors by looking at real world data. I think, in problems where the prior is important and complicated, data-driven approaches have a big advantage over hand-engineered approaches. That does not mean they will take over every kind of problem.
- deleted 9y ago
  [deleted]

by blt 9y ago

anonymfus 9y ago

> No such high-resolution examples exist for pixel art. How about application icons?

blt 9y ago
good point!

murukesh_s 9y ago

waifu2x seems to be based on Deep Convolutional Neural Networks, while the article looks like a pure handwritten algorithm and at least in this case the algorithm looks like a winner.. I am wondering whether in the future we may forget about writing algorithms altogether and instead rely on ANNs to do tasks that could have done better using hand written algorithms?

blt 9y ago
You mean tasks like computing the rank of a matrix? (http://oneweirdkerneltrick.com/rank.pdf http://oneweirdkerneltrick.com/rank.pdf) ;) Image super-resolution is an ill-posed inverse problem: many possible high-resolution images would be reduced to the same low-resolution image. However, some of the possibilities are more realistic than others. Consider a grey pixel in a photograph from real life. It's more likely that it was downsampled from a 2x2 block of grey pixels than from a 2x2 black-and-white checkerboard pattern. We apply such knowledge by adding regularization to the problem, or using a prior distribution in a Bayesian formulation. Deep learning is very good at memorizing those priors by looking at real world data. I think, in problems where the prior is important and complicated, data-driven approaches have a big advantage over hand-engineered approaches. That does not mean they will take over every kind of problem.
deleted 9y ago
[deleted]