Switching bilateral filter with a texture noise detector for universal noise removal
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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

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Switching bilateral filter with a texture noise detector for universal noise removal

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
Outline Universal Noise Removal

  • 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
Introduction Universal Noise Removal

  • 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
-Noise Universal Noise Removalmodels

  • The Impulse noise corrupted pixel ui,j:

    • Salt-and-pepper: ni,jonly takes values of Lmin or Lmax.

    • Uniform impulse: ni,jtakes random values from the interval [Lmin , Lmax]with a uniform distribution.

  • The Gaussian noise corrupted pixelui,j:

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


Sorted quadrant median vector for noise detection
Sorted Quadrant Median Vector for Noise Universal Noise RemovalDetection

The processing window size is too small.

  • 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-freepixel.

    • 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.


Definition of sorted quadrant median vector sqmv
-Definition of Sorted Quadrant Median Vector (SQMV Universal Noise Removal)

  • 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:


Definition of sorted quadrant median vector sqmv1
-Definition of Sorted Quadrant Median Vector (SQMV Universal Noise Removal)

  • 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 sortedin an ascending order.


Features of sqmv
-Features Universal Noise Removalof SQMV


Features of sqmv1
-Features Universal Noise Removalof SQMV


Features of sqmv2
-Features Universal Noise Removalof SQMV


Features of sqmv3
-Features Universal Noise Removalof SQMV


Edge texture identification with the clusters of sqmv
-Edge/Texture Universal Noise Removalidentification with the clusters of SQMV

  • The differencebetween two boundary values:

ρ lies in the interval [25–40]


Edge texture identification with the clusters of sqmv1
-Edge/Texture Universal Noise Removalidentification with the clusters of SQMV

  • Experimental result:


Reference median
-Reference Universal Noise Removalmedian

  • 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:


Reference median1
-Reference Universal Noise Removalmedian

  • The pixel selection of x1~x4:

  • Thereference median (SQMR)in each case:

Even if complextexture , the filtering

result would be less artificial.

“without edge” or “weak edge”

“edge or texture”


Switching bilateral filter
Switching Bilateral Universal Noise RemovalFilter

  • 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
-Switching Universal Noise Removalscheme

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

  • The filtered image is defined as follows:

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


Noise detector design
-Noise Universal Noise Removaldetector design

  • The noise detection :

    • The threshold:

      • For salt-and-pepperimpulse noise: [Tk1 Tk2] =[3015]

      • For uniform impulse and Gaussiannoise: [Tk1 Tk2] =[255]


Switching bilateral filter1
-Switching Universal Noise Removalbilateral filter

  • Propose a new universal noise removalalgorithm: 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 Universal Noise RemovalResults


Experimental results1
Experimental Universal Noise RemovalResults


Experimental results2
Experimental Universal Noise RemovalResults


Experimental results3
Experimental Universal Noise RemovalResults


Experimental results4
Experimental Universal Noise RemovalResults


Conclusions
Conclusions Universal Noise Removal

  • 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.