1 / 27

Advanced Image Noise Restoration Techniques: A Comprehensive Overview

This extensive overview delves into image noise restoration methods, including noise sources, estimation of noise parameters, filters for noise reduction, order statistics filters, and adaptive median filters. Learn about the various types of noise models, noise parameters estimation, and filter comparisons, along with detailed algorithms for adaptive median filtering and addressing periodic noise using specific filter types.

lucian-rodgers
Télécharger la présentation

Advanced Image Noise Restoration Techniques: A Comprehensive Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Image Restoration - Focus on Noise

  2. References • Gonzales and Wood second edition • Jain

  3. Enhancement - Restoration

  4. Overview Measured From [1] Unknown Approximation

  5. Noise sources • Device noise (often thermal) • Digitization process • Sampling and quantization • Transmission • Environment

  6. Noise models • White noise: autocorrelation is an impulse • Colored noise • Usually assume that noise is uncorrelated with the image • Gaussian: circuit noise, illumination, environment (thermal) • Rayleigh: range imaging • Uniform: easy to model • Others: exponential, impulse (salt and pepper)

  7. Sample pdfs From [1]

  8. Test image 3 distinct gray levels From [1]

  9. Additive Noise Noise is added to the respective gray levels. Hence the multiple lobe histograms From [1]

  10. Additive Noise From [1]

  11. Estimation of Noise Parameters – Periodic Noise • Periodic noise – filter in frequency domain. Appears as pair of impulses. The removal can be automated when the impulses are more pronounced. From [1]

  12. Noise Parameter Estimation – Known Model • Noise parameters can be computed by focusing on small sub-image (patch). From [1]

  13. Mean and S.D. estimation

  14. Image Restoration – Noise Only Degradation Use Filters: Spatial Filter n(x,y) is unknown. For periodic noise, N(u,v) can be estimated from G(u,v) – spikes at predominant noise frequencies.

  15. Noise Reduction Filters Noise Reduction Filters

  16. Applying Arithmetic and Geometric Filters From [1]

  17. More Noise Reduction Filters

  18. Comparisons of Filters • Arithmetic: Smoothing reduces noise. Blurring. • Geometric: Smoothing. Less loss of image detail than Arithmetic. • Harmonic: Reduces salt noise. No impact on pepper noise. • Contraharmonic: Reduces salt and pepper noise. Q>0 reduces pepper noise. Q<0 reduces salt noise. Cannot reduce salt and pepper noise in the same pass. Q = 0 yields Arithmetic Q = -1 yields Harmonic

  19. Order Statistics Filters

  20. Multiple applications of the Median Filter From [1]

  21. Order Statistics Filters - 2

  22. Adaptive Filter – Reduce Local Noise

  23. Arithmetic, Geometric and Adaptive Filters From [1]

  24. Adaptive Median Filter • Preserve detail. • Smooth non-impulse noise {different from tradition median filter}. • Like Adaptive Filter use a window Sxy. • The center of the window is replaced by the result • Unlike Adaptive Filter, the size of the window is increased. • Notation zmin = min gray level in Sxy. zmax = max gray level in Sxy. zmed = median gray level in Sxy. zxy = gray level at coordinate (x,y). Smax = max allowed size of Sxy.

  25. Adaptive Median Filter Level A: { is zmed an impulse?} while window size is less than Smax do if zmed > zmin AND zmed < zmax, then Go To Level B else increase the window size end while output zxy Level B: { is zxy an impulse?} if zxy > zmin AND zxy < zmax, then output zxy else output zmed • Algorithm objectives • Remove salt and pepper noise • Smooth other noise • Reduce distortions, e.g. excessive thinning or thickening of boundaries

  26. Adaptive Median Filter From [1]

  27. Periodic Noise • Band reject filters • Band pass filters • Notch filters

More Related