Abstract
Iterative methods are very successful for denoising images corrupted by random valued impulse noise. However, choosing the optimal number of iterations is a difficult issue. In this letter, a stopping method is proposed: the iterative denoising process is stopped when the number of cleaned pixels is minimal. It corresponds to the moment when the denoising process tends to modify noise-free pixels. It also corresponds with a high precision to the maximum of PSNR of the restored image. The originality of the method is that no a priori iteration number is to be chosen but the method results from image information. The proposed stopping strategy is therefore an efficient and image dependent method that can be easily implemented on real data.
References
Akkoul, S., Lédée, R., Leconge, R., Léger, C., Harba, R., Pesnel, S., Lerondel, S., Lepape, A., & Vilcahuaman, L. (2008). Comparison of image restoration methods for bioluminescence imaging. ICISP 08, Cherbourg, France, LNCS 5099, pp. 163–172
Akkoul S., Lédée R., Leconge R., Harba R. (2010) A new adaptive switching median filter. IEEE Signal Processing Letters 16(6): 587–590
Brownrigg D. (1984) The weighted median filter. Communications of the Association for Computing Machinery 27: 807–818
Chen T., Ma K. K., Chen L. H. (1999) Tri-state median filter for image denoising. IEEE Transactions on Image Processing 8: 1834–1838
Dong Y., Xu S. (2007) A new directional weighted median filter for removal of random-value impulse noise. IEEE Signal Processing Letters 14(3): 193–196
Gonzalez R. C., Woods R. E. (2002) Digital image processing. Prentice-Hall, Englewood Cliffs, NJ
Kang, C. C., & Wang, W. J. (2008) Modified switching median filter with one more noise detector for impulse noise removal. International Journal of Electronics and Communications. doi:10.1016/j.aeue.2008.08.009.
Ko S. J., Lee Y. H. (1991) Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits System 38: 984–993
Ng P. -E., Ma K. -K. (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing 15(6): 1506–1516
Pesnel S., Akkoul S., Lédée R., Leconge R., Pillon A., Kruczynski A., Harba R., Lerondel S., Le Pape A. (2011) Use of an image restoration process to improve spatial resolution in bioluminescence imaging. Molecular Imaging 10(6): 446–452
Sun T., Neuvo Y. (1994) Detail preserving median based filters in image processing. Pattern Recognition Letters 15: 341–347
Wan Y., Chen Q., Kang Y. (2010) Robust impulse noise variance estimation based on image histogram. IEEE Signal Processing Letters 17(5): 485–488
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Akkoul, S., Harba, R. & Lédée, R. An image dependent stopping method for iterative denoising procedures. Multidim Syst Sign Process 25, 611–620 (2014). https://doi.org/10.1007/s11045-012-0204-5
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DOI: https://doi.org/10.1007/s11045-012-0204-5