Denoising of Natural Images: Optimality and Fundamental Lower Bounds

author: Boaz Nadler, Weizmann Institute of Science
published: Jan. 12, 2011,   recorded: December 2010,   views: 513
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

In natural image denoising, the task is to estimate a clean version of a given noisy image, using prior knowledge on the statistics of natural images. The problem has been studied intensively with impressive progress achieved in recent years. However, it seems that image denoising has reached a plateau, with new algorithms improving over previous ones by only fractional dB values. A key question is thus: How much more can current methods be improved? In this talk we’ll discuss optimal natural image denoising and its fundamental lower bounds. In particular, we’ll show that at moderate noise levels, current state-of-theart denoising algorithms, that use a fixed small support window around each denoised pixel, are approaching optimality and cannot be further improved beyond fractional dB values. Joint work with Anat Levin.

See Also:

Download slides icon Download slides: nipsworkshops2010_nadler_dni_01.pdf (726.6 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: