### Making Use of a Prior Implicit in a Denoiser

This is a recent talk from Eero Simoncelli of NYU that covers examining and using a prior implicit in a denoiser. This is really a talk about human perception and perceptual representations of images that uses denoising as an entry into that exploration.

Let's check it out.

Observations:

1: Removing the bias in CNN makes it work better *(better generalization)*.

2: System learns adaptive filters (directional based on input statistics of source and noise).

3: Projection into a low dimensional space. Sharp scale space, not blurry.

4: Spatial derivatives at the high end of the subspace.

5: Visual images lie on a low-dimensional surface (manifold).

6: Using a denoiser to do iterative gradient ascent to get to a manifold surface. This ends up being a generative model of natural images.

7: Adding a little bit of injected noise to #6 makes it converge better.

8: You can use this 'learned denoiser system' to solve linear inverse problems. Problems like inpainting, random pixel dropping, super-resolution, random pojections.

*Not sure i get that these tasks are called linear? Linear in a basis set?*

Slick!

The paper titled 'Solving linear inverse problems using the prior implicit in a denoiser' can be found here.

## Comments

## Post a Comment