Computer Vision – Computer Vision – Enhancement(Part II)Enhancement(Part II)
Hanyang University
Jong-Il Park
Department of Computer Science and Engineering, Hanyang University
Local EnhancementLocal Enhancement
Global enhancement The same operation for all pixels
Local enhancement Different operation for each pixel According to the statistics of local support
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Local Histogram EqualizationLocal Histogram Equalization
Using a fixed window at each point Computationally expensive
Histogram equalization at each point
Department of Computer Science and Engineering, Hanyang University
Use of statistics of local supportUse of statistics of local support
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Originalimage
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Spatial OperationsSpatial Operations
Spatial averaging and spatial LPF for noise smoothing
Inputimage *
output
Spatial mask( 33, 55, )
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Spatial MaskSpatial Mask
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Spatial AveragingSpatial Averaging
Mean-filtering
Noise reduction
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Spatial Averaging MaskSpatial Averaging Mask
Spatial averaging masks a(k,l)
Disadvantage : blurring
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Effect of window sizeEffect of window size
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Eg. Spatial Averaging(1)Eg. Spatial Averaging(1)
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Eg. Spatial Averaging(2)Eg. Spatial Averaging(2)
Original image Averaging 후의 image
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Cf. Multi-imageCf. Multi-image averagingaveraging
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Spatial Operations - FilteringSpatial Operations - Filtering
Parametric Low Pass Filter
but to preserve the mean
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Spatial LPF, BPF, HPFSpatial LPF, BPF, HPF
Spatialaveraging
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(c) Spatial band-pass filter
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LPHP
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Original image Lowpass Filter 된 후의 image
Eg. Spatial LPFEg. Spatial LPF
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Spatial High-Pass FilteringSpatial High-Pass Filtering
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Original image Highpass filtered image
Eg. Spatial HPFEg. Spatial HPF
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Original image Lowpass Filter(Short Term) =A
Lowpass Filter(Long Term) =B
Bandpass Filter 된 후의 Image =B-A
Spatial Band-Pass FilteringSpatial Band-Pass Filtering
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Denoising by LPFDenoising by LPF
Noisy! Blurred! Trade-off?
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Directional SmoothingDirectional Smoothing
Directional Smoothing to protect the edges from blurring while smoothing
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Department of Computer Science and Engineering, Hanyang University
Original image Lowpass Filter(Long Term)
Direc. Smoothing ( 대각선 ) Direc. Smoothing ( 수 직 )
Eg. Directional SmoothingEg. Directional Smoothing
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Median FilteringMedian Filtering
Median Filter
Properties nonlinear filter
Example
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filter length should be odd number
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Eg. 1D Median FilteringEg. 1D Median Filtering
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Discussion – Median filterDiscussion – Median filter
1) median filter preserve discontinuities in a step function
2) smooth a few pixels whose values differ significantly from the surrounding, without affecting the other pixels.
3) pulse function, whose width is less than one half the filter length, are suppressed
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2D Median Filtering2D Median Filtering
Original Image
Filtered Image
Filtered Image
Filter
Filter
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Eg. Median FilteringEg. Median Filtering
Original 7x7 Median filtered image
Salt-and-pepper noise(=impulsive noise)
Excellent performance!
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Eg. Median Filter – Impulsive NoiseEg. Median Filter – Impulsive Noise
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Eg. Median Filter – Impulsive NoiseEg. Median Filter – Impulsive Noise
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Eg. Median Filter – Gaussian NoiseEg. Median Filter – Gaussian Noise
Moderate performance
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Various patterns for median filterVarious patterns for median filter
Neighborhood patterns used for median filtering
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Eg. Median filter – Square patternEg. Median filter – Square pattern
Original image 10% black, 10% white
Median filtering using 3 by 3 square region
Median filtering using 5 by 5 square region
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Eg. Median filter – Octagon patternEg. Median filter – Octagon pattern
Original image 5 by 5 octagonal median filter
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Eg. Median filter – Reconstruction Eg. Median filter – Reconstruction
Original image Median filtering and color compensation
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Sharpening ImagesSharpening Images
Emphasis of high-frequency components Usually exploiting 1st order derivative and 2nd order
derivatives
1D derivatives 1st order derivative:
2nd order derivative:
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f
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Eg. 1st & 2nd order derivativesEg. 1st & 2nd order derivatives
Department of Computer Science and Engineering, Hanyang University
Observation on derivativesObservation on derivatives
2nd order derivative Thinner edges Stronger response to fine details Weaker response to a gray-level step Double response at step changes Intensity of response: point > line > step
The 2nd order derivative is better suited than the 1st order derivative for image enhancement.
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Laplacian Operator – Derivation Laplacian Operator – Derivation
The simplest isotropic derivative operator
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Department of Computer Science and Engineering, Hanyang University
Laplacian OperatorLaplacian Operator
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Sharpening by Laplacian operatorSharpening by Laplacian operator
Department of Computer Science and Engineering, Hanyang University
Eg. SharpeningEg. Sharpening
Original SEM image Laplacian operator Subtraction of the Laplacian from the original
Original image Laplacian operator Subtraction of the Laplacian from the original
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Composite Laplacian maskComposite Laplacian mask
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Signal
Low-pass
High-pass(1)
(2)
(3)
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Unsharp masking and CrispeningUnsharp masking and Crispening
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Unsharp mask applicationUnsharp mask application
Original image Processed image
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High-boost filteringHigh-boost filtering
Let g(n1, n2) = u(n1, n2) - uL(n1, n2)
v(n1, n2) = u(n1, n2) + k g(n1, n2)
k=1: Unsharp Masking Crispening an image
k>1: High-boost filtering edge or line details to be emphasized
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Eg. High-boost filteringEg. High-boost filtering
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Zoom(1:2 magnification) revisitedZoom(1:2 magnification) revisited
Nearest neighbor=Replication = zero - order hold
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Zoom revisited(cont.)Zoom revisited(cont.)
Linear Interpolation : first - order hold
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