U-Net to Fix Your Grainy Photos?

Recently watched “Research at NVIDIA: AI Can Now Fix Your Grainy Photos by Only Looking at Grainy Photos” https://www.youtube.com/watch?v=pp7HdI0-MIo and was impressed!

Did some searching and was able to get PDF “Noise2Noise: Learning Image Restoration without Clean Data” https://arxiv.org/pdf/1803.04189.pdf which mentions “Our baseline is a recent state-of-the-art method ”RED30” (Mao et al., 2016), a 30-layer hierarchical residual network with 128 feature maps, which has been demonstrated to be very effective in a wide range of image restoration tasks, including Gaussian noise…For all further tests, we switch from RED30 to a shallower U-Net (Ronneberger et al., 2015) that is roughly 10× faster to train and gives similar results (−0.2 dB in Gaussian noise).”

Since U-Net is so much faster only checked it and found PDF “U-Net: Convolutional Networks for Biomedical Image Segmentation” https://pdfs.semanticscholar.org/0704/5 … 912281.pdf which mentions “The u-net architecture achieves very good performance on very different biomedical segmentation applications…We provide the full Caffe[6]-based implementation and the trained networks4. We are sure that the u-net architecture can be applied easily to many more tasks.”

Continued search and also found “U-Net: Convolutional Networks for Biomedical Image Segmentation” https://lmb.informatik.uni-freiburg.de/ … index.html there is a short video and at bottom of page “We provide the u-net for download in the following archive: u-net-release-2015-10-02.tar.gz (185MB). It contains the ready trained network, the source code, the matlab binaries of the modified caffe network, all essential third party libraries, the matlab-interface for overlap-tile segmentation and a greedy tracking algorithm used for our submission for the ISBI cell tracking challenge 2015. Everything is compiled and tested only on Ubuntu Linux 14.04 and Matlab 2014b (x64)” Hope it will be ported to Windows!!! it also mentions “If you do not have a CUDA-capable GPU or your GPU is smaller than mine, edit segmentAndTrack.sh accordingly (see there for documentation).”

I do not read either French or German so do not know if this has been addressed in these language Forums but did search the English Forum and did not find any mention of U-Net.

Has anyone tried U-Net?

Thanks

Ken

No, I haven’t tried U-Net; however, there is active research on algorithms that can develop super low light images as if they were well exposed to begin with. Some are using resources like U-Net and Caffe.

That is insane if you think about it. That is, when the image turns out to be what we expect; not sure the success rates though.

afre,

“No, I haven’t tried U-Net; however, there is active research on algorithms that can develop super low light images as if they were well exposed to begin with. Some are using resources like U-Net and Caffe.”

Have you tried any AI Image noise reduction software?

This is new to me so checked Caffe wiki Caffe (software) - Wikipedia

As you said there is a Lot of Activity. “In April 2017, Facebook announced Caffe2,[12] which includes new features such as Recurrent Neural Networks. At the end of March 2018, Caffe2 was merged into PyTorch[13]”

Even linked to Comparison of deep learning software Comparison of deep-learning software - Wikipedia No mention of U-Net?

Thanks for your comments!

Googled using keywords AI for “image noise reduction” AI for "image noise reduction" - Google Suche got 16K results. Reduced to last year.

Found “Topaz Labs Releases the First Deep Learning Photo Noise Removal Tool for Windows and Mac” Topaz Labs Releases the First Deep Learning Photo Noise Removal Tool for Windows and Mac | Business Wire Press Release for Topaz Labs AI Clear™ Examples at https://topazlabs.com/ai-clear

Could not find how AI Clear™ affects Min hardware requirements.

Ken

Topaz Labs sells one- / few- click solutions with hyperbolic promises. In the past, I have played with their trials and was left unsatisfied.

BTW, what lead you to be interested in this topic? What’s your story? For me, I am just interested in reading about all sorts of subjects.