Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

Chih-Hsing Lin, Jia-Shiuan Tsai, and Ching-Te Chiu Transactions on: Image Processing, IEEE Journals 2010

Outline• Introduction• Sorted Quadrant Median Vector for Noise Detection

– Noise models– Definition of Sorted Quadrant Median Vector (SQMV)– Features of SQMV– Edge/Texture identification with the clusters of SQMV– Reference median

• Switching Bilateral Filter– Switching scheme– Noise detector design– Switching bilateral filter

• Experimental Results• Conclusions

Introduction• Gaussian noise: a zero-mean Gaussian

distribution.– Effective filter: linear filters (ex: averaging)– Side effect: blurring

• Impulse noise: replacing a portion of an image pixels with noise values.– Effective filter: nonlinear filters (ex: median)

• In this paper, we propose a universal noise removal filter based upon the “detect and replace” methodology.

-Noise models

• The Impulse noise corrupted pixel ui,j :

– Salt-and-pepper: ni,j only takes values of Lmin or Lmax.– Uniform impulse: ni,j takes random values from the interval

[Lmin , Lmax] with a uniform distribution.

• The Gaussian noise corrupted pixel ui,j :

• In this paper, mixed impulse and Gaussian noise is considered, and the Gaussian noise is independent of impulse noise.

Sorted Quadrant Median Vector for Noise Detection

• Motivation of the Noise Detection Scheme:– Existing two-state noise

detectors fail in several conditions[9][17].

– The central pixel of (a)(b) identified as noise-free pixel.

– The medians of (c) stillsimilar.

[9] T. Chen and H. R. Wu, “Adaptive impulse detection using center-weighted median filters,” IEEE Signal Process. Lett., vol. 8, no. 1, pp. 1–3, Jun. 2001.[17] P. E. Ng and K. K. Ma, “A switching median filter with boundary discriminative noise detection for extremely corrupted images,” IEEE Trans. Image Process., vol. 15, no. 6, pp. 1506–1516, Jun. 2006.

The processing window size is too small.

-Definition of Sorted Quadrant Median Vector (SQMV)

• To overcome the problems, we propose a sorted quadrant median vector (SQMV):– For a (2N+1) *(2N+1) window we divide the

window into four (N+1)*(N+1) subwindows .– In the case N = 2:

• The set of points can be expressed as:– For (2N+1) *(2N+1) window:

– For (N+1) *(N+1) subwindows:

– Where the SQMV is defined as:

• SQM1, SQM2, SQM3 and SQM4 are the medians m1, m2, m3, and m4 sorted in an ascending order.

-Definition of Sorted Quadrant Median Vector (SQMV)

-Features of SQMV

-Features of SQMV

-Features of SQMV

-Features of SQMV

-Edge/Texture identification with the clusters of SQMV

• The difference between two boundary values:

ρ lies in the interval [25–40]

• Experimental result:

-Edge/Texture identification with the clusters of SQMV

-Reference median• In “without edge” or “weak edge” cases, the

reference median (SQMR) for xij is the average of SQM2 and SQM3 (major cluster).

• In “edge or texture” case, decide which cluster the current pixel xij falls into by dav:

• The pixel selection of x1~x4:

• The reference median (SQMR) in each case:

-Reference median

“without edge” or “weak edge”

“edge or texture”

Even if complex texture , the filteringresult would be less artificial.

Switching Bilateral Filter

• Bilateral Filter:

– xi,j: the current pixel ̶ yi,j: the filtered pixel– xi+s,j+t : he pixels in (2N+1)*(2N+1) window

-Switching scheme

• In the switching scheme, we the noise detector searches for noisy pixels and tries to distinguish them from uncorrupted ones.

• The filtered image is defined as follows:

– S1 and S2: the binary control signals generated by the noise detector.

• The noise detection :

– The threshold:

• For salt-and-pepper impulse noise: [Tk1 Tk2] = [30 15]

• For uniform impulse and Gaussian noise: [Tk1 Tk2] = [25 5]

-Noise detector design

-Switching bilateral filter

• Propose a new universal noise removal algorithm: the switching bilateral filter (SBF)

– Parameter selection:• For “edge” σS = 3,

otherwise σS = 1.

• σR = [30,50] will work well, we choose σR = 40.

Experimental Results

Experimental Results

Experimental Results

Experimental Results

Experimental Results

Conclusions

• Propose SQMV for edge/texture detection, noise detection and switching bilateral filter.

• The noise detector shows a good performance in identifying noise even in mixed noise models.

• In most of the noise model cases, proposed filter outperforms both in PSNR and visually.