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Merav Kass January 2003

Blind Inverse Gamma Correction (Hany Farid , IEEE Trans. Signal Processing, vol. 10 no. 10, October 2001) An article review. Merav Kass January 2003. Inverse Gamma Correction - Motivation. Imaging device non linearity character. Gamma correction:.

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Merav Kass January 2003

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  1. Blind Inverse Gamma Correction(Hany Farid , IEEE Trans. Signal Processing, vol. 10 no. 10, October 2001)An article review Merav Kass January 2003

  2. Inverse Gamma Correction - Motivation • Imaging device non linearity character. • Gamma correction: • Inverse gamma correction – an advantageous to SP applications.

  3. Blind Inverse Gamma Correction - Motivation • If g is known: The imaging device calibration information The need in blind inverse gamma correction arise! • Typically, g is determined experimentally.

  4. What? & How? What is a blind inverse Gamma correction ? • It is an estimation process. • No prior knowledge is assumed. How does it work ? • Minimize higher-order correlation in the frequency domain.

  5. Higher Order Correlation Modified Signal Original Signal Gamma Correction

  6. Higher order correlations in the frequency domain Deviation of Gamma from unity Higher order correlations Gamma 1

  7. How higher order correlations can be measured ? By estimating the bicoherence function: It reveals the sort of higher order correlations introduced by nonlinearity.

  8. The Algorithm Assumptions • Only one parameter has to be estimated : gamma. • The only thing we have to work with is the a gamma corrected image.

  9. The Algorithm Apply inverse Operation Measure Correlations Course of action

  10. Experimental Results Before After g = 0.42 g = 0.80 On Average, the correct gamma is estimated within 7.5% of the actual value. g = 1.10 g = 1.63 g = 2.11

  11. Additional Notes • C(g) is a well behaved function. • Calculation efficiency. • The algorithm performance in presence of additive noise. • The algorithm performance in presence of linear transformations. • Colored images.

  12. Restrictions and Limitations • One parameter model is assumed. • The procedure assume to be uniform.

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