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

3. Classic Median Filtering 20% salt-and-pepper noise median filter (5 × 5 mask size)

5. Peak Signal to Noise Ratio − restored image pixel value − original image pixel value − image pixels number

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. Switching Image Restoration Schema corrupted image restored image preliminary detection step filtration procedure

9. Salt-and-Pepper Impulse Noise Model - corrupted image pixel values - original image pixel values - negative impulses density - positive impulses density

11. Proposed MPSM Algorithm for Salt-and-Pepper Noise Removal (noise detector) − input corrupted image − binary matrix noise detection result

12. Proposed MPSM Algorithm for Salt-and-Pepper Noise Removal (filtration procedure) restored image

13. Comparative Analysis of Efficiency (classic algorithms)

14. Comparative Analysis of Efficiency (algorithms with detector)

15. Visual Analysis Original image 20% salt-and-pepper noise (PSNR = 10.68 dB)

16. Visual Analysis AMF (PSNR = 33.94 dB) MPSM (PSNR = 36.77 dB)

17. Highly Corrupted Image Restoration 50% salt-and-pepper noise ( PSNR = 8,45 dB) MPSM ( PSNR = 33 . 22 dB )

18. Highly Corrupted Image Restoration 80% salt-and-pepper noise ( PSNR = 6 . 42 dB ) MPSM ( PSNR = 25 . 83 dB )

19. Random Valued Impulse Noise Model - corrupted image pixel values - original image pixel values - noise density - uniformly distributed random value

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. Proposed MPSM Algorithm for Random Valued Impulse Noise Removal (noise detector) noise detection result − input corrupted image − binary matrix

23. Proposed MPSM Algorithm for Random Valued Impulse Noise Removal (filtration procedure) restored image

24. Random Valued Impulse Noise Removal Results

25. Visual Results 15% random valued impulse noise (PSNR = 17,47 dB) SDROM (PSNR = 34,70 dB)

26. Visual Results DWM (PSNR = 35,28 dB) Proposed Algorithm (PSNR = 35,91 dB)

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. PICLAB (www.piclab.ru) PICLAB – is the advanced tool for image restoration and research of image processing algorithms