Image signal-to-noise ratio and noise variance estimation using autoregressive model

Scanning

Volumn 26, Issue 6, 2004, Pages 277-281

  Kamel, Nidal S ^a   Sim, K S ^a  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

Author keywords

Autoregressive model; Parameter estimation; Scanning electron microscope; White additive Gaussian noise

Indexed keywords

ESTIMATION; IMAGE ANALYSIS; IMAGING SYSTEMS; REMOTE SENSING; SCANNING ELECTRON MICROSCOPY; STATISTICAL METHODS;

AUTOREGRESSIVE (AR) MODEL; IMAGE NOISE VARIANCE; NOISE VARIANCE ESTIMATION;

SIGNAL TO NOISE RATIO;

ARTICLE; IMAGING; IMAGING SYSTEM; MATHEMATICAL MODEL; NOISE; PRIORITY JOURNAL; REMOTE SENSING; SCANNING ELECTRON MICROSCOPY; SIGNAL NOISE RATIO; SIMULATION; TECHNIQUE; VARIANCE;

ESTIMATION; IMAGE ANALYSIS; NOISE; REMOTE SENSING; SIGNALS; STATISTICAL ANALYSIS;

EID: 10144220663     PISSN: 01610457     EISSN: None     Source Type: Journal    
DOI: 10.1002/sca.4950260605     Document Type: Article

References (8)

1. Balakrishnan A: Kalman Filtering Theory. Optimization Software. Springer: New York (1984)
2. Canny J: A computational approach to edge detection. IEEE Trans Pattern Analysis and Machine Intelligence 8, 679-698 (1986)
3. Frank J, Al-Ali L: Signal-to-noise ratio of electron micrographs obtained by cross correlation. Nature 376-379 (1975)
4. Meer P, Jolion J, Rosenfeld A: A fast parallel algorithm for blind estimation of noise variance. Trans Pattern Analysis and Machine Intelligence 12, No. 2, February 216-223 (1990)
5. Rosenfeld A, Kak A: Digital Picture Processing, 2nd ed. Academic Press, Orlando, Fla. (1982)
6. Sahoo P, Soltani S, Wong A: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41, 233-260 (1988)
7. Sim KS, Kamel NS: Image signal-to-noise ratio estimation using the autoregressive model. J Scan Microsc 26, 135V139 (2004)
8. Voorthees H, Poggio T: Detecting blobs as textons in natural images. Proc Image Understanding Workshop Los Angeles, Feb. 22-25, 892-899 (1987)