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IMAGE restoratıon

IMAGE restoratıon. Robot Vision Prof . Dr. Hasan OCAK. Image blur model. Frequency domaın model. INVERSE fılterıng. EXAMPLE: Inverse fılterıng. İdeal Bölütleme. Original Image Blurred Image Restored with H -1 ( u , v ). Blurred with Gaussian σ = 0.5.

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IMAGE restoratıon

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  1. IMAGE restoratıon Robot Vision Prof. Dr. Hasan OCAK

  2. Image blur model

  3. Frequency domaın model

  4. INVERSE fılterıng

  5. EXAMPLE: Inverse fılterıng İdeal Bölütleme Original Image Blurred Image Restored with H -1(u, v) Blurred with Gaussian σ = 0.5

  6. EXAMPLE: Inverse fılterıng İdeal Bölütleme Original Image Blurred Image Restored with H -1(u, v) Blurred with Gaussian σ = 1

  7. EXAMPLE: Inverse fılterıng İdeal Bölütleme Original Image Blurred Image Restored with H -1(u, v) Blurred with Gaussian σ = 2 A small amount of noise saturates the inverse filter

  8. Inverse fılterıng

  9. Pseudo Inverse fılterıng • Identify regions where H (u, v) is close to zero and set the inverse filter’s response to zero at those frequencies. • This will help reduce noise amplification.

  10. EXAMPLE: Pseudo Inverse fılterıng

  11. Wıener fılter

  12. EXAMPLE: Wıener fılter

  13. EXAMPLE: Wıener fılter

  14. Wıener fılter response

  15. APPLICATIOn: readıng lıcense plate ALGORITHM: • Rotate image so that blur is horizontal • Estimate length of blur • Compute and apply Wiener filter • Optimize over values of K

  16. APPLICATIOn: readıng lıcense plate

  17. perıodıc noıse reductıon Magnitude Spectrum Original Image Band-reject Filter Restored Image

  18. Bılateral fılter

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