Image Super-Resolution Using Deep Convolutional Networks · HackerPapers discussions

Image Super-Resolution Using Deep Convolutional Networks — mentioned by digitalemble72

Wed, 01 Feb 2017 03:30:05 GMT

I think it's important to note that all of these methods have been largely superseded by deep learning techniques. For example, we can now directly learn algorithms such as gradient descent[1] and classical inverse problems like superresolution are solvable with deep networks [2]. While there still may be a role for tools like CVX, I anticipate all future progress will come from end-to-end differentiable systems. [1] https://arxiv.org/abs/1606.04474 [2] https://arxiv.org/abs/1501.00092

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by ingenter

Mon, 16 Jan 2017 23:01:50 GMT

Here's a list of various image interpolation techniques: - http://www.ifp.illinois.edu/~jyang29/papers/chap1.pdf * Image super-resolution: Historical overview and future challenges - http://www.robots.ox.ac.uk/~vgg/publications/papers/pickup08... * Machine Learning in Multi-frame Image Super-resolution - http://www.cs.huji.ac.il/~peleg/papers/icpr90-SuperResolutio... - http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html - http://chiranjivi.tripod.com/EDITut.html - http://www.tecnick.com/pagefiles/appunti/iNEDI_tesi_Nicola_A...…

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by hatsunearu

Tue, 15 Nov 2016 02:41:51 GMT

https://github.com/nagadomi/waifu2x Here's a previous project that attempted this as well. https://arxiv.org/abs/1501.00092

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by lotyrin

Mon, 14 Nov 2016 22:08:45 GMT

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_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 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.)

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by ingenter

Sat, 29 Oct 2016 11:33:22 GMT

Here's a list of various image interpolation techniques, with similar goal: - http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html - http://chiranjivi.tripod.com/EDITut.html - http://www.tecnick.com/pagefiles/appunti/iNEDI_tesi_Nicola_A... - http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/conte... - http://bengal.missouri.edu/~kes25c/ http://bengal.missouri.edu/~kes25c/nnedi3.zip http://forum.doom9.org/showthread.php?t=147695 - http://arxiv.org/pdf/1501.00092v2.pdf http://waifu2x.udp.jp/ https://github.com/nagadomi/waifu2x http://waifu2x-avisynth.sunnyone.org/…

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by buymorechuck

Fri, 21 Aug 2015 04:07:45 GMT

There is a more recent version of this paper published here: http://arxiv.org/abs/1501.00092v3 At Flipboard, we did not have time to do a full comparison of related upscaling research, but we were happy with the low amount of error our CNN achieved.

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Image Super-Resolution Using Deep Convolutional Networks — mentioned by buymorechuck

Fri, 21 Aug 2015 04:00:40 GMT

FWIW, waifu2x was inspired by this paper from researchers at Chinese University of Hong Kong and Microsoft Research Asia http://arxiv.org/abs/1501.00092v3 waifu2x is a great demonstration of this approach applied to a specific domain. By coincidence, Flipboard's DNN approach was developed around the same time as the MSRA research in summer 2014. I'm excited to see future research in applying deep learning to generative tasks. Some of the CNN music composition work is quite impressive.

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