Restoration of images corrupted by Gaussian and uniform impulsive noise

Pattern Recognition

Volumn 43, Issue 5, 2010, Pages 1835-1846

  López Rubio, Ezequiel ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Gaussian noise; Image restoration; Kernel regression; Probabilistic mixture models; Uniform impulsive noise

Indexed keywords

BAYESIAN CLASSIFICATION; DEGRADATION PROCESS; DIGITAL TRANSMISSION; GAUSSIAN NOISE; GAUSSIANS; IMAGE RESTORATION; IMPULSIVE NOISE; KERNEL REGRESSION; PROBABILISTIC MIXTURE MODELS; RESTORATION OF IMAGES;

GAUSSIAN NOISE (ELECTRONIC); IMAGE RECONSTRUCTION; OPTICAL COMMUNICATION; REGRESSION ANALYSIS; RESTORATION;

IMPULSE NOISE;

EID: 75749154947     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.11.017     Document Type: Article

References (56)

1. Ben-Ezra M., and Nayar S.K. Motion-based motion deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 6 (2004) 689-698
2. Schuon S., and Diepold K. Comparison of motion de-blur algorithms and real world deployment. Acta Astronautica 64 11-12 (2009) 1050-1065
3. Choi B.-D., Jung S.-W., and Ko S.-J. Motion-blur-free camera system splitting exposure time. IEEE Transactions on Consumer Electronics 54 3 (2008) 981-986
4. Shin T., Nielsen J.-F., and Nayak K. Accelerating dynamic spiral MRI by algebraic reconstruction from undersampled k-t space. IEEE Transactions on Medical Imaging 26 7 (2007) 917-924
5. Zhang J., Zhang Q., and He G. Blind deconvolution of a noisy degraded image. Applied Optics 48 12 (2009) 2350-2355
6. Rabbani H. Image denoising in steerable pyramid domain based on a local Laplace prior. Pattern Recognition 42 9 (2009) 2181-2193
7. Russo F. A method for estimation and filtering of Gaussian noise in images. IEEE Transactions on Instrumentation and Measurement 52 4 (2003) 1148-1154
8. Ghazal M., Amer A., and Ghrayeb A. Structure-oriented multidirectional Wiener filter for denoising of image and video signals. IEEE Transactions on Circuits and Systems for Video Technology 18 12 (2008) 1797-1802
9. Bovik A.C. Handbook of Image and Video Processing (Communications, Networking and Multimedia) (2005), Academic Press, Inc., Orlando, FL, USA
10. Figueiredo M., Bioucas-Dias J., and Nowak R. Majorization-minimization algorithms for wavelet-based image restoration. IEEE Transactions on Image Processing 16 12 (2007) 2980-2991
11. V. Bruni, D. Vitulano, Combined image compression and denoising using wavelets, Signal Processing: Image Communication 22 (1).
12. Zhang J.-S., Huang X.-F., and Zhou C.-H. An improved kernel regression method based on Taylor expansion. Applied Mathematics and Computation 193 2 (2007) 419-429
13. Takeda H., Farsiu S., and Milanfar P. Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16 2 (2007) 349-366
14. Dengwen Z., and Wengang C. Image denoising with an optimal threshold and neighbouring window. Pattern Recognition Letters 29 11 (2008) 1694-1697
15. Sendur L., and Selesnick I. Bivariate shrinkage with local variance estimation. IEEE Signal Processing Letters 9 12 (2002) 438-441
16. Luisier F., Blu T., and Unser M. A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Transactions on Image Processing 16 3 (2007) 593-606
17. Pizurica A., and Philips W. Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising. IEEE Transactions on Image Processing 15 3 (2006) 654-665
18. Alajlan N., Kamel M., and Jernigan E. Detail preserving impulsive noise removal. Signal Processing: Image Communication 19 10 (2004) 993-1003
19. Kong H., and Guan L. A neural network adaptive filter for the removal of impulse noise in digital images. Neural Networks 9 3 (1996) 373-378
20. Luo W. An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Processing Letters 13 7 (2006) 413-416
21. Kang C.-C., and Wang W.-J. Fuzzy reasoning-based directional median filter design. Signal Processing 89 3 (2009) 344-351
22. Wang S.-S., and Wu C.-H. A new impulse detection and filtering method for removal of wide range impulse noises. Pattern Recognition 42 9 (2009) 2194-2202
23. Lukac R. Adaptive vector median filtering. Pattern Recognition Letters 24 12 (2003) 1889-1899
24. Besdok E. A new method for impulsive noise suppression from highly distorted images by using Anfis. Engineering Applications of Artificial Intelligence 17 5 (2004) 519-527
25. Lin R.-S., and Hsueh Y.-C. Multichannel filtering by gradient information. Signal Processing 80 2 (2000) 279-293
26. Liu C., Szeliski R., Kang S.B., Zitnick C.L., and Freeman W.T. Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 2 (2008) 299-314
27. Ibrahim H., Kong N., and Ng T.F. Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Consumer Electronics 54 4 (2008) 1920-1927
28. Gabay A., Kieffer M., and Duhamel P. Joint source-channel coding using real BCH codes for robust image transmission. IEEE Transactions on Image Processing 16 6 (2007) 1568-1583
29. Redinbo G. Decoding real block codes: activity detection Wiener estimation. IEEE Transactions on Information Theory 46 2 (2000) 609-623
30. Rodriguez P., and Wohlberg B. Efficient minimization method for a generalized total variation functional. IEEE Transactions on Image Processing 18 2 (2009) 322-332
31. Bar L., Brook A., Sochen N., and Kiryati N. Deblurring of color images corrupted by impulsive noise. IEEE Transactions on Image Processing 16 4 (2007) 1101-1111
32. Bovik A., Huang T., and Munson J. A generalization of median filtering using linear combinations of order statistics. IEEE Transactions on Acoustics, Speech and Signal Processing 31 6 (1983) 1342-1350
33. Yu H., Zhao L., and Wang H. An efficient procedure for removing random-valued impulse noise in images. IEEE Signal Processing Letters 15 (2008) 922-925
34. Shih-Chang Hsia, A fast efficient restoration algorithm for high-noise image filtering with adaptive approach, Journal of Visual Communication and Image Representation 16 (3) (2005) 379-392.
35. Ahmet Oguz Akyüz, Erik Reinhard, Noise reduction in high dynamic range imaging, Journal of Visual Communication and Image Representation 18 (5) (2007) 366-376, special issue on High Dynamic Range Imaging.
36. Rabie T. Adaptive hybrid mean and median filtering of high-iso long-exposure sensor noise for digital photography. Journal of Electronic Imaging 13 2 (2004) 264-277
37. Inoue I., Tanaka N., Yamashita H., Yamaguchi T., Ishiwata H., and Ihara H. Low-leakage-current and low-operating-voltage buried photodiode for a CMOS imager. IEEE Transactions on Electron Devices 50 1 (2003) 43-47
38. Garnett R., Huegerich T., Chui C., and He W. A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing 14 11 (2005) 1747-1754
39. Ji L., and Yi Z. A mixed noise image filtering method using weighted-linking PCNNs. Neurocomputing 71 13-15 (2008) 2986-3000
40. Hogg R.V., Craig A., and McKean J.W. Introduction to Mathematical Statistics. sixth ed. (2004), Prentice Hall
41. Hastie T., Tibshirani R., and Friedman J.H. The Elements of Statistical Learning. second ed. (2009), Springer
42. Wand M.P., and Jones M.C. Kernel Smoothing (Monographs on Statistics and Applied Probability) (1994), Chapman & Hall/CRC
43. Kay S.M. Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (1993), Prentice Hall PTR
44. Ando S. Image field categorization and edge/corner detection from gradient covariance. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 2 (2000) 179-190
45. Chen Y.X.C.G. Impulse noise suppression with an augmentation of ordered difference noise detector and an adaptive variational method. Pattern Recognition Letters 30 4 (2009) 460-467
46. Agaian D.E.P.K. Logical system representation of images and removal of impulse noise. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 38 6 (2008) 1349-1362
47. Dinuzzo D.N.G. An algebraic characterization of the optimum of regularized kernel methods. Machine Learning 74 3 (2009) 315-345
48. Wang Z., and Zhang D. Progressive switching median filter for the removal of impulse noise. IEEE Transactions on Circuits and Systems 46 1 (1999) 78-80
49. Srinivasan K.S., and Ebenezer D. A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Processing Letters 14 3 (2007) 189-192
50. University of Waterloo repertoire of images, In Internet: 〈http://links.uwaterloo.ca/colorset.base.html〉, 2008.
51. E. Hamilton, JPEG file interchange format (Version 1.02), C-Cube Microsystems, 1992.
52. Sikora T. MPEG digital video coding standards. IEEE Signal Processing Magazine 14 (1997) 82-100
53. López-Rubio E., Ortiz-de-Lazcano-Lobato J.M., and López-Rodríguez D. Probabilistic PCA self-organizing maps. IEEE Transactions on Neural Networks 20 9 (2009) 1474-1489
54. Bishop C.M. Pattern Recognition and Machine Learning (Information Science and Statistics) (2006), Springer
55. Evans M., Hastings N., and Peacock B. Statistical Distributions. third ed. (2000), Wiley, New York
56. Kacker R.N., and Lawrence J.F. Trapezoidal and triangular distributions for Type B evaluation of standard uncertainty. Metrologia 44 2 (2007) 117-127