image noise iii - university of arizonadial/ece533/notes14.pdfece/opti533 digital image processing...

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ECE/OPTI533 Digital Image Processing class notes 278 Dr. Robert A. Schowengerdt 2003 IMAGE NOISE III Periodic, Nonstationary Noise • Example: Mariner 9 (Chavez, 1975) Generated by Ultraviolet Spectrometer (UVS) motor during image readout • Model: , f s mn , ( 29 fmn , ( 29 f η mn , ( 29 fmn , ( 29 fmn , ( 29η mn , ( 29 = = 1 η mn , ( 29 1 published original and processed images

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ECE/OPTI533 Digital Image Processing class notes 278 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIPeriodic, Nonstationary Noise

• Example: Mariner 9 (Chavez, 1975)

Generated by Ultraviolet Spectrometer (UVS) motor during image readout

• Model:

, fs m n,( ) f m n,( ) fη m n,( )– f m n,( ) f m n,( )η m n,( )–= = 1– η m n,( ) 1≤ ≤

published original and processed images

ECE/OPTI533 Digital Image Processing class notes 279 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIProcedure (modifed from Chavez, 1975)

Periodic, nonstationary noise removal

• Estimate noise period

• Create noise “template”

• Correlate template with noisy image

• Multiply correlation map and noisy image Ñ> estimated noise map

• Subtract weighted, estimated noise map from noisy image

ECE/OPTI533 Digital Image Processing class notes 280 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE III noise template (16x zoom) - 3 periods of sinewave with period = 27 pixels

normalized correlation between noisy image and noise template

normalized correlation times noisy image - estimated noise map

border effectfrom correlation

ECE/OPTI533 Digital Image Processing class notes 281 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE III

noisy image processed image

ECE/OPTI533 Digital Image Processing class notes 282 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIDetector Striping• Model: “global” DN statistics from each detector should be the

same

• Adjust gain and offset for each detector to equalize global statistics

Choose one detector’s statistics, or the average statistics over all detectors, as “reference”

ECE/OPTI533 Digital Image Processing class notes 283 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE III

line-by-line average DN

detector mean DN std dev DN1 69.96 44.632 62.67 35.653 73.19 48.884 87.59 53.00

ECE/OPTI533 Digital Image Processing class notes 284 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE III

• If detectors have nonlinear gain or offset, use CDF-based reference contrast stretch (see Notes page 6-12)

destriped to equal mean DN and standard deviation DN

global mean DN = 69.59global std dev DN = 44.63

(detector 1 is reference)

ECE/OPTI533 Digital Image Processing class notes 285 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIDetector Banding

• Use combination of LPFs and HPFs (Crippen, 1989)

Heuristic algorithm for Landsat TM (16 detectors/scan)

1 row x 101 column LPF

33 row x 1 column HPF

1 row x 31 column LPF

***

noisyimage

cleanedimage

isolates low-frequency noise

isolates high-frequency banding

reduces diagonal artifacts from HPF

ECE/OPTI533 Digital Image Processing class notes 286 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIexample

• “Cosmetic”-type algorithm, not based on calibration data

• Filter sizes must be adapted to particular sensor detector configuration

original

water-masked

101 column LP 33 row HP

31 column LP water-masked

filtered

water-masked

ECE/OPTI533 Digital Image Processing class notes 287 Dr. Robert A. Schowengerdt 2003

IMAGE NOISE IIIReferences

• Chavez, P. S., Jr. (1975). “Simple high-speed digital image processing to remove quasi-coherent noise patterns.” in: , Washington, D.C., American Society of Photogrammetry: 595-600.

• Crippen, R. E. (1989). “A simple spatial filtering routine for the cosmetic removal of scan-line noise from Landsat TM P-tape imagery.” 55(3): 327-331.

• Eliason, E. and A. S. McEwen (1990). “Adaptive box filter for removal of random noise from digital images.” 56(4): 453-458.

• Lee, J. (1983). “Digital image smoothing and the sigma filter.” 24: 255-269.

• Lee, J.-S. (1980). “Digital image enhancement and noise filtering by use of local statistics.” PAMI-2(2): 165-168.

• Lee, J.-S. (1981). “Speckle analysis and smoothing of synthetic aperture radar images.” 17: 24-32.

• Nagao, M. and T. Matsuyama (1979). “Edge preserving smoothing.” 9: 394-407.

• O’Handley, D. A. and W. B. Green, “Recent Developments in Digital Image Processing at the Image Processing Laboratory at the Jet Propulsion Laboratory,” Proc. IEEE, Vol. 60, No. 7, July 1972, pp. 821-828.

• Rindlfeish, T. C., J. A. Dunne, H. J. Frieden, W. D. Stromberg, and R. M. Ruiz, “Digital Processing of the Mariner 6 and 7 Pictures,” , Vol. 76, No. 2, January 10, 1971, pp. 394-417.

• Schowengerdt, R. A., , Second Edition, Academic Press, 1997.