Adaptive Non-linear Filtering Technique for Image Restoration

Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.





References:
[1] J. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital
Filtering, CRC Press, New York, NY, USA, 1977.
[2] I. Pitas and A. N. Venetsanopoulos, Nonlinear Digital Filters: Principles
and Applications, Kluwer Academic Publishers, Boston, Mass, USA,
1990.
[3] S. M. Shahrokhy, "Visual and statistical quality assessment and
improvement of remotely sensed images," in Proceedings of the 20th
Congress of the International Society for Photogrammetry and Remote
Sensing (ISPRS '04), pp. 1-5, Istanbul, Turkey, July 2004.
[4] A. U. Silva and L. Corte-Real, "Removal of blotches and line scratches
from film and video sequences using a digital restoration chain," in
Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and
Image Processing (NSIP '99), pp. 826-829, Antalya, Turkey, June 1999.
[5] A. Kokaram, "Detection and removal of line scratches in degraded
motion picture sequences," in Proceedings of the 8th European Signal
Processing Conference (EUSIPCO '96), vol. 1, pp. 5-8, Trieste, Italy,
September 1996.
[6] H.-M. Lin and A. N. Willson, Jr., "Median filters with adaptive length,"
IEEE Transactions on Circuits and Systems, vol. 35, no. 6, pp. 675-690,
1988.
[7] H. Hwang and R. A. Haddad, "Adaptive median filters: new algorithms
and results," Transactions on Image Processing, vol. 4, no. 4, pp. 499-
502, 1995.
[8] T. Chen and H. R. Wu, "Adaptive impulse detection using centerweighted
median filters," IEEE Signal Processing Letters, vol. 8, no. 1,
pp. 1-3, 2001.
[9] W. Luo, "An efficient detail-preserving approach for removing impulse
noise in images," IEEE Signal Processing Letters, vol. 13, no. 7, pp.
413-416, 2006.
[10] K. S. Srinivasan and D. Ebenezer, "A new fast and efficient decisionbased
algorithm for removal of high-density impulse noises," IEEE
Signal Processing Letters, vol. 14, no. 3, pp. 189-192, 2007.
[11] Y. Nie and K.-K. Ma, "Adaptive rood pattern search for fast blockmatching
motion estimation," IEEE Transactions on Image Processing,
vol. 11, no. 12, pp. 1442-1449, 2002.
[12] S.-J. Ko and Y. H. Lee, "Center weighted median filters and their
applications to image enhancement," IEEE Transactions on Circuits and
Systems, vol. 38, no. 9, pp. 984-993, 1991.
[13] D. A. F. Florencio and R. W. Schafer, "Decision-based median filter
using local signal statistics," in Visual Communications and Image
Processing '94, vol. 2308 of Proceedings of SPIE, pp. 268-275, Chicago,
Ill, USA, September 1994.
[14] T. Chen, K.-K. Ma, and L.-H. Chen, "Tri-state median filter for image
denoising," IEEE Transactions on Image Processing, vol. 8, no. 12, pp.
1834-1838, 1999.
[15] E. Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, "A new efficient
approach for the removal of impulse noise from highly corrupted
images," IEEE Transactions on Image Processing, vol. 5, no. 6, pp.
1012-1025, 1996.