Digital Image Processing Fundamental
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Transcript of Digital Image Processing Fundamental
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DIGITAL IMAGE PROCESSINGPART IThuong Nguyen
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CONTENT
Digital image fundamentals Image transform Image enhancement Image restoration Image compression
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I. DIGITAL FUNDAMENTAL
Digital Image Processing System Sampling and Quantization Relationships between pixels
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DIP SYSTEM
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DIP SYSTEM
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DIP SYSTEM
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SAMPLING AND QUANTIZATION
Quantization: limit of intensity resolution Sampling: Limit of spatial and temp resolution
Uniform and non-uniform
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PIXEL’S RELATIONSHIPS
Two pixel are adjacent if Neighbors as 4, 8, and m-connectivity Gray levels satisfy a specified criterion
Connectivity Existing a path between two pixels
Path Path from p(x,y) to q(s,t) is
Where (x, y) = (x0, y0), (s, t) = (xn, yn)(x0, y0), (x1, x2), …, (xn, yn)
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II. IMAGE ENHANCEMENT IN FREQ DOMAIN
Discrete Fourier Transform Other Image Transform Hotelling Transform
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THE DISCRETE FOURIER TRANSFORM
The Fourier transform 1-D 2-D
Properties
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THE DISCRETE FOURIER TRANSFORM Discrete Fourier transform pair
One dimensional
Two dimensional
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THE DISCRETE FOURIER TRANSFORM
2D FFT and Image Processing
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THE DISCRETE FOURIER TRANSFORM
1. Multiply input image by 2. Compute , DFT3. Multiply by
4. Compute IDFT5. Obtain the real part6. Multiply the result by
Fast Fourier transform Efficient algorithm to compute DFT by reduce computation
burden: O(N2) – O(NlogN)
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OTHER SEPARABLE IMAGE TRANSFORM
General relation ship
Several condition Separable Symmetric
Separable kernel can be compute in two step of 1D transf
For separable and symmetric kernel
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OTHER SEPARABLE IMAGE TRANSFORM
Walsh Transform
Hadamard transform
Discrete cosine transform
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HOLTELLING TRANSFORM
1
2
.
.
n
x
x
x
x
1
1{ }
M
x kk
m E x xM
x,........,
M data points
1 M
1
1{( )( ) }
MT TT
x x x k k k kk
C E x m x m x x m mM
Mean:
Covariance:
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III. IMAGINE ENHANCEMENT
Basic intensity functions Histogram processing Spatial Filtering Enhancement in the Frequency domain Color image processing
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BASIC INTENSITY FUNCTIONS
Spatial domain process
Image negatives: intensity level in the range [0, L-1] s = L – 1 – r
Log trans s = c log(1 + r)
Power law (gramma) trans s = c r
Piecewise-Linear Trans Contrast stretching Intensity level slicing Bit plane slicing
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HISTOGRAM PROCESSING
Histogram Histogram equalization: Histogram matching Local histogram processing
Image subtraction Image averaging
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SPATIAL FILTERING Fundamental: using spatial masks for Image Processing
Smoothing Filter Lowpass spatial filtering Meadian filtering
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SPATIAL FILTERING Sharpening filter
Highpass spatial filtering Emphasize fine details
High-boost filtering Enhance high freq while keeping the low freq Highboost = (A-1) original + Highpass
Derivative filters First order: gradient
Second order
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ENHANCEMENT IN THE FREQUENCY DOMAIN
Spatial domain Definition
Chang pixel position changes in the scene
Distance is real
Processing Directly process the input image
pixel array
Frequency domain Definition
Change in image position changes in spatial frequency
Which image intensity values are changing in the spatial domain image
Processing Transform the image to its frequency
representation Perform image processing compute
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ENHANCEMENT IN THE FREQUENCY DOMAIN
Lowpass filter Ideal
Butterword
Highpass filter Ideal
Butterworth
Homomorphic
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COLOR IMAGE PROCESSING
Background Human can perceive thousands of colors Two major area: full color and pseudo color Color quantization: 8-bit or 24bit
Color fundamental Result of light in the rentina: 400-700nm Characterization of light: monochromatic and gray level
Radiance: total amount of energy emitted by light source Brightness: intensity Luminance: amount of energy perceived by obervers, in lumens
Color characters Hue Saturation Birghtness
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IV. IMAGE RESTORATION
Degradation Model Diagonalization of Circulant & Block-Circulant Matrices Algebraic Approach Inverse Filtering Weiner Filter Constrained LS Restoration Interactive Restoration Restoration at Spatial Domain Geometric transform
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Noise models Spatial and frequency properties Noise PDF: Gaussian, Rayleigh, Erlang, Exponential, Uniform,
Impulse .. Estimate noise parameters:
Spectrum inspection: periodic noise Test image: mean, variance and histogram shape, if imaging system is
available
De-noising Spatial filtering ( for additive noise)
Mean filters Order-statistics filters Adaptive filters:
Frequency domain filtering (for periodic noise)
DEGRADATION MODEL
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V. IMAGE COMPRESSION
Fundamentals Image Compression Models Elements of Information Theory Error-Free Compression Lossy Compression Image Compression standard
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VI. IMAGE SEGMENTATION
Detection of Discontiuties Edge Linking and Boundary Detection Thresholding Region-Oriented Segmentation Motion in Segmentation
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VII. REPRESENTATION AND DESCRIPTION
Representation Scheme Boundary Descriptors Regional Descriptors Morphology Relational Descriptors
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VIII. RECOGNITION AND INTERPRETATION
Elements of Image Analysis Patterns and Pattern Classes Decision-Theoretic Methods Structural Methods Interpretation