
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
P. Meer, J.M. Jolion, A. Rosenfeld, "A Fast Parallel Algorithm for Blind Estimation of Noise Variance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 2, pp. 216223, February, 1990.  
BibTex  x  
@article{ 10.1109/34.44408, author = {P. Meer and J.M. Jolion and A. Rosenfeld}, title = {A Fast Parallel Algorithm for Blind Estimation of Noise Variance}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {2}, issn = {01628828}, year = {1990}, pages = {216223}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.44408}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Fast Parallel Algorithm for Blind Estimation of Noise Variance IS  2 SN  01628828 SP216 EP223 EPD  216223 A1  P. Meer, A1  J.M. Jolion, A1  A. Rosenfeld, PY  1990 KW  computerised picture processing; image pyramids; fast parallel algorithm; blind noise variance; noise image; tessellations; outlier analysis; variance estimate sequence; computerised picture processing; estimation theory; noise; parallel processing VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A blind noise variance algorithm that recovers the variance of noise in two steps is proposed. The sample variances are computed for square cells tessellating the noise image. Several tessellations are applied with the size of the cells increasing fourfold for consecutive tessellations. The four smallest sample variance values are retained for each tessellation and combined through an outlier analysis into one estimate. The different tessellations thus yield a variance estimate sequence. The value of the noise variance is determined from this variance estimate sequence. The blind noise variance algorithm is applied to 500 noisy 256*256 images. In 98% of the cases, the relative estimation error was less than 0.2 with an average error of 0.06. Application of the algorithm to differently sized images is also discussed.
[1] A. V. Balakrishnan,Kalman Filtering Theory. New York: Springer, 1984.
[2] P. J. Besl and R. C. Jain, "Segmentation through variableorder surface fitting,"IEEE Trans. Pattern Anal. Machine Intell., vol. 10, pp. 167192, 1988.
[3] W. H. Beyer, Ed.,CRC Handbook of Tables for Probability and Statistics, 2nd ed. Boca Raton, FL: CRC Press, 1968.
[4] P. Brodatz,Textures. New York: Dover, 1966.
[5] J. F. Canny, "A computational approach to edge detection,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI8, pp. 679697, 1986.
[6] H. A. David,Order Statistics. New York: Wiley, 1970.
[7] W. D. Hillis,The Connection Machine. Cambridge, MA: MIT Press, 1985.
[8] H. A. H. Ibrahim, "Pyramid algorithms on the Connection Machine," inProc. Image Understanding Workshop, Cambridge, MA, Apr. 68, 1988. Morgan Kaufmann, pp. 634639.
[9] P. Meer, J. M. Jolion, and A. Rosenfeld, "A fast parallel algorithm for blind estimation of noise variance," Comput. Vision Lab., Univ. Maryland, College Park, Rep. CARTR373, June 1988.
[10] P. Meer, S. Wang, and H. Wechsler, "Edge detection by associative mapping,"Pattern Recogn., vol. 22, pp. 491503, 1989.
[11] T. Poggio, J. Little, E. Gamble, W. Gillett, D. Geiger, D. Weinshall, M. villalba, N. Larson, T. Cass, H. Bulthoff, M. Drumheller, P. Oppenheimer, W. Yang, and A. Hurlbert, "The MIT vision machine," inProc. Image Understanding Workshop, Cambridge, MA, Apr. 68, 1988. Morgan Kaufmann, pp. 177198.
[12] A. V. Oppenheim and R. W. Schafer,Digital Signal Processing. Englewood Cliffs, NJ: PrenticeHall, 1975.
[13] A. Rosenfeld, Ed.,Multiresolution Image Processing and Analysis. Berlin: Springer, 1984.
[14] A. Rosenfeld and A. Kak,Digital Picture Processing, New York: Academic, 1976.
[15] P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding techniques,"Comput. Vision Graphics Image Processing, vol. 41, pp. 233260, 1988.
[16] H. Voorhees and T. Poggio, "Detecting blobs as textons in natural images, " inProc. Image Understanding Workshop, Los Angeles, CA, Feb. 2325, 1987. Morgan Kaufmann, pp. 892899.