1

SURE-LET for Orthonormal Wavelet-DomainVideo Denoising

Florian Luisier, Member, IEEE, Thierry Blu, Senior Member, IEEE, and Michael Unser, Fellow, IEEE

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 20, NO. 6, JUNE 2010

2

Outline• Introduction• SURE-LET principle

– Stein’s Unbiased Risk Estimate (SURE)– Linear Expansion of Thresholds (LET)

• Algorithm– Global Motion Compensation– Local Motion Compensation by Selective Block-Matching– Multiframe Interscale Wavelet Thresholding– Computational Complexity

• Experiments• Conclusion• Result

3

Introduction

• An efficient orthonormal wavelet-domain video denoising algorithm based on an integration of motion compensation into an adapted version SURE-LET approach.

• The results are even competitive with most state-of-the-art redundant wavelet-based techniques.

4

SURE-LET principle

• This approach avoids any a priori hypotheses on the noise-free signal.

5

Stein’s Unbiased Risk Estimate (SURE)

• SURE [18] is an unbiased statistical estimate of the mean squared error (MSE).

6

Linear Expansion of Thresholds (LET)

7

Algorithm

8

Global Motion Compensation

• To increase the correlations between adjacent frames, we compensate for interframe motion using a global motion compensation

9

Local Motion Compensation by Selective Block-Matching(1/5)

• The proposed selective block-matching procedure has two key advantages:– Fast – The interframe noise covariance matrix can be

assumed to be unaffected by the local motion compensation contrary to standard block-matching

10

Local Motion Compensation by Selective Block-Matching(2/5)

• parameters are therefore involved:

1) The size of the considered blocks: 8X16 2) The size of the search region: 15X15 3) The criterion used for measuring the

similarity between blocks: MSE 4) The way of exploring the search region: Exhaustive search

11

Local Motion Compensation by Selective Block-Matching(3/5)

• Perform motion compensation only in the blocks, where a significant motion between frames was detected.

12

Local Motion Compensation by Selective Block-Matching(4/5)

• The block-matching itself is performed on the smoothed frames, in order to decrease the sensitivity to noise.

13

Local Motion Compensation by Selective Block-Matching(5/5)

• The minimum of these MSEs (MSEmin) is considered as the “no motion level.”– Threshold λ2 MSEmin, where λ2 ≥ 1.

• Select λ1 = λ2 =

14

Multiframe Interscale Wavelet Thresholding

we experimentally found that λ3 = λ2 = λ1 = gave the best result.

15

Computational Complexity

16

Experiments

17

Experiments

18

Experiments

19

Conclusion

• Present a relatively simple and efficient video denoising algorithm.

• Favorably compare with most state-of-the-art redundant wavelet-based approaches, having a lighter computational load.

• Increase the shift-invariance of the proposed solution to reach the same level performance as the very best video denoising algorithm.

20

Result

• http://bigwww.epfl.ch/luisier/VideoDenoising/