Types of noise
- 1. Image Noise Dr. Robert A. Schowengerdt Techniques for Image Processing and Classifications in Remote Sensing Remote Sensing By KeTang
- 2. APPLICATIONS zSignal estimation in presence of noise zDetecting known features in a noisy background zCoherent (periodic) noise removal
- 3. TYPES OF NOISE ¾Photoelectronic ¾photon noise ¾thermal noise ¾Impulse ¾salt noise ¾pepper noise ¾salt and pepper noise ¾line drop ¾Structured ¾periodic, stationary ¾periodic, nonstationary ¾aperiodic ¾detector striping ¾detector banding
- 4. Photonelectronicnoise Photon noise ¾Photon arrival statistics ¾Low-light levels (nighttime imaging, astronomy) •Poisson density function•Standard deviation = (signal-dependent) ¾High-light levels (daytime imaging) •Poisson distribution > Gaussian distribution•Standard deviation = square root meanThermal noise ¾Electronic ¾White (flat power spectrum), Gaussian distributed, zero-mean (signal-independent) ()(|,) ! pTTePpTp ρρρ − = Tρ
- 5. Photoelectronicnoise model ¾Photon noise is signal-dependent ¾Thermal noise is signal-independent ¾One model for acombinednoise fieldare independent white, zero-mean Gaussian noise fieldsfsis the noiseless signal (may not be measurable) ),(),(),(),(nmnmfnmnmfTspηηη+= Tη pη
- 6. Noisy image model zadditive signal-dependent and signal- independent random noiseNote, this model may not apply in particular situations! ),(),(),(),( ),(),(),( nmnmfnmnmfnmfnmfnmfTspss ηηη ++= +=
- 7. Examples of simulated thermal noise for different noise standard deviations
- 8. Examples of simulated photon + thermal noise for different standard deviations
- 9. Impulse Noise zData loss or saturation zDefinitions Salt noise: DN = maximum possible Pepper noise: DN = minimum possible Salt and pepper noise: mixture of salt and pepper noise Line drop: part or all of a line lost
- 10. Structured Noise ¾Periodic, stationary ™Noise has fixed amplitude, frequency and phase ™Commonly caused by interference between electronic components
- 11. Structured Noise Mars Mariner Example
- 12. Structured Noise zPeriodic, nonstationary znoise parameters (amplitude, frequency, phase) vary across the image zIntermittent interference between electronic components
- 13. Structured Noise Mars Mariner 9 example zsingle frequency, variable amplitude (Chavez and Soderblum,1975)
- 14. Structured Noise zAperiodic ™JPEG noise ™ADPCM (Adaptive Pulse Code Modulation) noise
- 15. Structured Noise zDetector Striping Calibration differences among individual scanning detectors
- 16. Structured Noise zDetector Banding Calibration changes from scan-to-scan (whiskbroom scanner)
- 17. PhotoelectronicNoise zFrame averaging ¾If available, average N frames of same object ¾If noise is independent frame-to-frame, variance will be reduced by ¾Requires multiple, co-registered framesN/2η σ
- 18. Simulation Example Of Frame Averaging
- 19. Low-pass Smoothing zLow-pass smoothing Reduces high- frequency noise Smoothsimage Set filter cutoff at about SNR = 1
- 20. Sigma Filter zAverage selected pixels within moving window zAverage only those pixels that are within a threshold difference Δ from the DN of the center pixel, DNc zDNc+Δ
- 21. sigma filter near edges and lines
- 22. Nagao-Matsuyamafilter zCalculate the variance of 9 subwindowswithin a 5 x 5 moving window zOutput pixel is the mean of the subwindowwith the lowest variance
- 23. Example of SAR (Synthetic Aperture Radar) Noise Filtering
- 24. Example of SAR Noise Filtering
- 25. Example of SAR Noise Filtering
- 26. Impulse Noise zSalt and pepper noise DN is “outlier”relative to neighboring pixel DNs zUse algorithms that compare test pixel to neighbors
- 27. Noise cleaning zSet threshold Δ = kσglobal
- 28. Median filtering zExample of rank filtering zOutput DN = median(DNwindow) Length of window must be odd Sort input DNswithin window and select middle DN for output
- 29. Median Filter
- 30. Median Filter zseparable 2-D median filter preserves 2-D edges
- 31. Median Filter
- 33. Median Filter on Photoelectronic Noise
- 34. Structured Noise zPeriodic, stationary ¾Periodicity means noise power is isolated into a few frequencies ¾Difficulty is in detecting noise power “spikes” ¾Visual detection works, but not practical for processing large number of images
- 37. Structured Noise zNot really automated filter design zTwo parameters must be supplied: ¾width of Gaussian HPF ¾power spectrum threshold for notch filter