Eurocon2009 Apalkov

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Eurocon2009 Apalkov

  1. 1. IMPROVED SWITCHING MEDIAN FILTER FOR IMPULSE NOISE REMOVAL Denis K. Kuykin, Vladimir V. Khryashchev, Ilya V. Apalkov e-mail: connect@piclab.ru P.G. Demidov Yaroslavl State University Digital Circuits and Signals Laboratory
  2. 2. Agenda <ul><li>Introduction </li></ul><ul><li>Proposed Algorithms </li></ul><ul><li>Research Results for Salt-and-Pepper Noise </li></ul><ul><li>Research Results for Random Valued Impulse Noise </li></ul><ul><li>Conclusion </li></ul>
  3. 3. Classic Median Filtering 20% salt-and-pepper noise median filter (5 × 5 mask size)
  4. 4. Introduction <ul><li>The problem is </li></ul><ul><li>to develop modified algorithms based on rank-order statistics and switching schema for restoration of digital images corrupted with salt-and-pepper and random valued impulse noises </li></ul><ul><li>to compare proposed algorithms with known ones </li></ul><ul><li>Conditions to the algorithms: </li></ul><ul><li>effectiveness with wide range of noise ratio </li></ul><ul><li>reasonable computational complexity </li></ul><ul><li>efficient impulse noise removal </li></ul><ul><li>preservation of object edges </li></ul>
  5. 5. Peak Signal to Noise Ratio − restored image pixel value − original image pixel value − image pixels number
  6. 6. Image Quality Criteria Correlation with Human Estimation 0,930 1 0,8828 0,8939 0,7905 0,8226 0,7931 Averaged value 0,9552 0,9239 0,9497 0,9397 0,9184 0,9478 Additive Gaussian noise 0,9624 0,8736 0,9445 0,8959 0,8824 0,944 Impulse noise 0,9467 0,8965 0,9063 0,8433 0.8170 0,8428 JPEG2000 0,8784 0,8608 0,8129 0,6223 0,7762 0,4882 JPEG 0,9077 0,8592 0,8563 0,6514 0,7135 0,7428 Gaussian blurring VIF UIQ-M PSNR-M SSIM UIQ PSNR Image quality metrics Distortion type
  7. 7. Switching Image Restoration Schema corrupted image restored image preliminary detection step filtration procedure
  8. 8. Proposed Algorithms <ul><li>Modified Progressive Switching Median Filter for Salt-and-Pepper Noise Removal </li></ul><ul><li>Modified Progressive Switching Median Filter for Random Valued Impulse Noise Removal </li></ul>
  9. 9. Salt-and-Pepper Impulse Noise Model - corrupted image pixel values - original image pixel values - negative impulses density - positive impulses density
  10. 10. Salt-and-Pepper Impulse Noise Model (known restoration algorithms) CLASSICAL MEDIAN FILTER <ul><li>preserves object edges </li></ul><ul><li>misses a lot of noisy pixels </li></ul><ul><li>corrupts “good” image pixels </li></ul>PROGRESSIVE SWITHING MEDIAN FILTER ( PSM ) (Wang Z., Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Trans. on Circuits systems – II (1) (1999). V. 46, pp. 78-80.) <ul><li>preserv e s object edge s </li></ul><ul><li>is unable to remove blotches from highly corrupted images </li></ul>ADAPTIVE MEDIAN FILTER ( AMF ) <ul><li>removes blotches from highly corrupted images </li></ul><ul><li>often corrupts object edges </li></ul><ul><li>high computational complexity </li></ul>
  11. 11. Proposed MPSM Algorithm for Salt-and-Pepper Noise Removal (noise detector) − input corrupted image − binary matrix noise detection result
  12. 12. Proposed MPSM Algorithm for Salt-and-Pepper Noise Removal (filtration procedure) restored image
  13. 13. Comparative Analysis of Efficiency (classic algorithms)
  14. 14. Comparative Analysis of Efficiency (algorithms with detector)
  15. 15. Visual Analysis Original image 20% salt-and-pepper noise (PSNR = 10.68 dB)
  16. 16. Visual Analysis AMF (PSNR = 33.94 dB) MPSM (PSNR = 36.77 dB)
  17. 17. Highly Corrupted Image Restoration 50% salt-and-pepper noise ( PSNR = 8,45 dB) MPSM ( PSNR = 33 . 22 dB )
  18. 18. Highly Corrupted Image Restoration 80% salt-and-pepper noise ( PSNR = 6 . 42 dB ) MPSM ( PSNR = 25 . 83 dB )
  19. 19. Random Valued Impulse Noise Model - corrupted image pixel values - original image pixel values - noise density - uniformly distributed random value
  20. 20. Random Valued Impulse Noise Model (known restoration algorithms) ADAPTIVE CENTRAL WEIGHTED MEDIAN FILTER (ACWM) (Chen T., Wu H. Adaptive impulse detection using center-weighted median filters // IEEE Signal Processing Letters, 2001. V. 8, № 1. P. 1-3. ) <ul><li>provides good results if noise density is small </li></ul>SIGNAL DEPENDED RANK ORDERED MEAN FILTER ( SDROM ) (Abreu E., Mitra S. A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images // Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP’95), 1995. V. 4, P. 2371-2374. ) <ul><li>preserv e s object edge s </li></ul><ul><li>provide relatively good visual perceived quality </li></ul><ul><li>removes blotches from highly corrupted images </li></ul>DIRECTED WEIGHTED MEDIAN FILTER ( DWM ) (Dong Y., Hu S. A new directional weighted median filter for removal of random-valued impulse noise // IEEE Signal Processing Letters, 2003. V. 14, № 3. P. 193-196. ) <ul><li>often loose effectiveness if noise density is high </li></ul><ul><li>may blur tiny objects on image </li></ul>
  21. 21. Random Valued Impulse Noise Detectors Comparison miss – number of corrupted pixels missed by detector false-hit – number of noise-free pixels marked by detector as corrupted 6749 10962 5119 7257 4501 3523 SDROM 2110 14351 1553 8134 1514 3372 ACWM 6715 10396 4217 8607 2212 5216 DWM false- hit miss false- hit miss false- hit miss 30% 20% 10% p
  22. 22. Proposed MPSM Algorithm for Random Valued Impulse Noise Removal (noise detector) noise detection result − input corrupted image − binary matrix
  23. 23. Proposed MPSM Algorithm for Random Valued Impulse Noise Removal (filtration procedure) restored image
  24. 24. Random Valued Impulse Noise Removal Results
  25. 25. Visual Results 15% random valued impulse noise (PSNR = 17,47 dB) SDROM (PSNR = 34,70 dB)
  26. 26. Visual Results DWM (PSNR = 35,28 dB) Proposed Algorithm (PSNR = 35,91 dB)
  27. 27. More… The MPSM algorithm for random valued impulse noise removal is described in more details in the paper: D. Kuykin, V. Khryashchev, A. Priorov. DETECTION AND RESTORATION OF RANDOM-VALUED IMPULSE NOISE CORRUPTED PIXELS which was submitted to participate in The 2009 International Workshop on Local and Non-Local Approximation in Image Processing (LNLA’2009) Also this paper, MatLab code and all results can be downloaded from http :// www.piclab.ru / research / mpsm.html
  28. 28. PICLAB (www.piclab.ru) PICLAB – is the advanced tool for image restoration and research of image processing algorithms
  29. 29. Conclusion <ul><li>Proposed modified algorithm for salt-and-pepper impulse noise removal based on switching schema and rank ordered statistics provide about 1-2 dB PSNR increasing relative to another filters </li></ul><ul><li>Proposed MPSM algorithm for restoration of images corrupted by random valued impulse noise provides more performance in image restoration which is expressed in 0.5 dB PSNR increasing. </li></ul>

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