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

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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

    • 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,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

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)

  • 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 (SQMV)

  • 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 of SQMV


-Features of SQMV


-Features of SQMV


-Edge/Texture identification with the clusters of SQMV

  • The differencebetween two boundary values:

ρ lies in the interval [25–40]


-Edge/Texture identification with the clusters of SQMV

  • Experimental result:


-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:


-Reference median

  • 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

  • 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 uncorruptedones.

  • The filtered image is defined as follows:

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


-Noise detector design

  • The noise detection :

    • The threshold:

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

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


-Switching bilateral 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 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.


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