denoising

If you are trying to do feature learning, you are using RBMs, Denoising AutoEncoders and you will use reconstruction entropy.

Cameron himself doesn't like film grain, and he did indeed sign off on the excessive denoising.

With contractive terms or denoising, the are powerful feature extractors as well.

Take SVM for example, or sparse signal denoising, or template matching.

I'm sorry, but I can't outsource the computation of real time ultrasound denoising to EC2.

One common set of assumptions in image denoising is that: 1.

If you can digitize the graph reliably, you can then take the discrete Fourier transform of the data, apply some denoising/thresholding to extract the main components.

There are some noise-handling pieces, but no public denoising API.

I'm pretty sure ultrasound denoising does not run on > 10000 cores.

(median works well if you have symmetrically distributed noise, which is true when denoising astronomy photos, for example, but not here).

For denoising autoencoders, watch your corruption level.

Speaking of video, I wonder how much grain came from the camera sensor - I'd start with some denoising, just to try to knock that out.

Also this includes pixar's "denoising as a post process" which is really amazing (and might even be applicable to cycles renders).

If you rephrase your question to "What happens when I add random noise to the inputs of a neural network and try to teach it to output a "denoised" version of the input." and you've just invented denoising autoencoders.