İmage enhancement prepare image for further processing steps for specific applications
DESCRIPTION
Image Enhancement in spatial domain Brightness Transform: 1. Position Dependent f(i,j)= g(i,j). e(i,j) g:Clean image e:position dependent noise 2. Gray scale TransformTRANSCRIPT
İmage enhancement
Prepare image for further processing steps for specific applications
Image enhancement: Pre-processing
• Spatial domain techniques: Find a transformation T
f(x,y) g(x,y)• Frequency domain techniques
• f(x,y) F(u,v) G(u,v) g(x,y) F-1F T
T
Image Enhancement in spatial domain
• Brightness Transform: 1. Position Dependent
f(i,j)= g(i,j). e(i,j)g:Clean imagee:position dependent noise
2. Gray scale Transform
Gray scale transform: s=T(r)
• r original color, s transformed color
L-1
L-1 r
sS=r
S=r
S=r
Gray Scale Transformq=T(p)
Binarize and contrast streching
Image Enhancement THRESHOLDING
Log Transform:q= clog (1+p)
Negation
Power law transform
Image Enhancement by Gray scale transform
Image Enhancement by Gray scale transform
Image Enhancement by Gray Scale Transform
Image Enhancement by Gray scale transform
Image Enhancement by Gray scale transform
Bit plane slicing• Soppose each pixel is represented by n-bits.• Represent each bit by a plane
Bit-plane slicingImage Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Histogram processing
• Given an image with L gray levels• h(rk) = nk
• rk: kth gray level
• nk: number of pixels with gray level rk
• Normalized histogramP(rk) = nk/NN:total number of pixels
Histograms of various image
Histogram Equalization
Find a transformation which yields a histogram with uniform density
?
Histogram of a dark image
Equalized Histogram
Specified Histogram
Local Histogram Equalization
Local ProcessingConvolution or Correlation: f*h
Define a mask and correlate it with the image
SMOOTHING
Image Enhancement WITH SMOOTING
Averaging blurrs the image
Image Enhancement WITH AVERAGING AND THRESHOLDING
Restricted Averaging
• Apply averaging to only pixels with brightness value outside a predefined interval.
Mask h(i,j) = 1 For g(m+i,n+j)€ [min, max]
0 otherwise
Q: Study edge strenght smoothing, inverse gradient and rotating mask
Median Filtering
• Find a median value of a given neighborhood.
• Removes sand like noise
0 2 12 1 23 3 2
0 2 12 2 23 3 2
0 1 1 2 2 2 2 3 3
Median filtering breaks the straight lines
5 5 5 5 55 5 5 5 50 0 0 0 05 5 5 5 55 5 5 5 5
Square filter:0 0 0 5 5 5 5 5 5
Cross filter0 0 0 5 5
Image Enhancement with averaging and median filtering
Image sharpening filters
Edge detectors
What is edge?
• Edges are the pixels where the brightness changes abrubtly.
• It is a vector variable with magnitude and direction
EDGE PROFILES
Continuous world first derivativeGradient
• Δg(x,y) = ∂g/ ∂x + ∂g/ ∂y• Magnitude: |Δg(x,y) | = √ (∂g/ ∂x)2 + (∂g/ ∂y) 2 • Phase : Ψ = arg (∂g/ ∂x , ∂g/ ∂y) radians
Discrete world derivatives: Gradient
• Use difference in various directions• Δi g(i,j) = g(i,j) - g(i+1,j)• or• Δj g(i,j) = g(i,j) - g(i,j+1)• or• Δij g(i,j) = g(i,j)- g(i+1,j+1)• or• |Δ g(i,j) | = |g(i,j)- g(i+1,j+1) | + |g(i,j+1)- g(i+1,j) |
Continuous world second derivativeLaplacian
• Δ2g(x,y) = ∂2g/ ∂2 x + ∂2 g/ ∂2 y
EDGES, GRADIENT AND LAPLACIAN
GRADİENT AND LAPLACIEN OF SMOOT EDGES, NOISY EDGES
GRADIENT EDGE MASKSApproximation in discrete grid
GRADIENT EDGE MASKS
Edge detection
Edge detection
Edge detection
LAPLACIAN MASKS
LAPLACIAN of GAUSSIAN EDGE MASKS
EDGE DETECTION
EDGE DETECTION
EDGE DETECTION
HOUGH TRANSFORM
PARAMETER PLANE OF HOUGH TRANSFORM
HOUGH TRANSFORM IN POLAR FORM
HOUGH TRANSFORM OF POINTS IN POLAR FORM
Chapter 10Image Segmentation
Chapter 10Image Segmentation
GRADIENT OPERATIONS
Image Enhancement WITH LAPLACIAN AND SOBEL
Image Enhancement (cont.)
Edg Detection with Laplacian
Image Enhancement with high pass filter
Edge Detection with High Boost
Laplacian Operator
Image Enhancement with Laplacian
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Histogram Equalization
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain
Chapter 3Image Enhancement in the
Spatial Domain