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The several fundamental of DIP

The several fundamental of DIP

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- 1. DIGITAL IMAGE PROCESSING PART I1 Thuong Nguyen
- 2. CONTENT Digital image fundamentals Image transform Image enhancement Image restoration Image compression 2
- 3. I. DIGITAL FUNDAMENTAL Digital Image Processing System Sampling and Quantization Relationships between pixels 3
- 4. DIP SYSTEM 4
- 5. DIP SYSTEM 5
- 6. DIP SYSTEM 6
- 7. SAMPLING AND QUANTIZATION Quantization: limit of intensity resolution Sampling: Limit of spatial and temp resolution Uniform and non-uniform 7
- 8. 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 (x0, y0), (x1, x2), …, (xn, yn) Where (x, y) = (x0, y0), (s, t) = (xn, yn) 8
- 9. II. IMAGE ENHANCEMENT IN FREQ DOMAIN Discrete Fourier Transform Other Image Transform Hotelling Transform 9
- 10. THE DISCRETE FOURIER TRANSFORM The Fourier transform 1-D 2-D Properties 10
- 11. THE DISCRETE FOURIER TRANSFORM Discrete Fourier transform pair One dimensional Two dimensional 11
- 12. THE DISCRETE FOURIER TRANSFORM 2D FFT and Image Processing 12
- 13. THE DISCRETE FOURIER TRANSFORM Fast Fourier transform Efficient algorithm to compute DFT by reduce computation 13 burden: O(N2) – O(NlogN)
- 14. 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 14
- 15. OTHER SEPARABLE IMAGE TRANSFORM Walsh Transform Hadamard transform Discrete cosine transform 15
- 16. HOLTELLING TRANSFORM Mean: M 1 x1 mx E{x} xk x2 M k 1x1 . ,........, x M Covariance: M . T 1 T T xn Cx E{( x mx )( x mx ) } xk xk mk mk M k 1 M data points 16
- 17. III. IMAGINE ENHANCEMENT Basic intensity functions Histogram processing Spatial Filtering Enhancement in the Frequency domain Color image processing 17
- 18. 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=cr Piecewise-Linear Trans Contrast stretching Intensity level slicing 18 Bit plane slicing
- 19. HISTOGRAM PROCESSING Histogram Histogram equalization: Histogram matching Local histogram processing Image subtraction Image averaging 19
- 20. SPATIAL FILTERING Fundamental: using spatial masks for Image Processing Smoothing Filter Lowpass spatial filtering Meadian filtering 20
- 21. 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 21
- 22. ENHANCEMENT IN THE FREQUENCY DOMAINSpatial domain Frequency domain Definition Definition Chang pixel position changes Change in image position changes in spatial frequency in the scene Which image intensity values are Distance is real changing in the spatial domain image Processing Processing Directly process the input image Transform the image to its pixel array frequency representation Perform image processing compute 22
- 23. ENHANCEMENT IN THE FREQUENCY DOMAIN Lowpass filter Ideal Butterword Highpass filter Ideal Butterworth Homomorphic 23
- 24. 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 24
- 25. 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 25
- 26. DEGRADATION MODEL 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: 26 Frequency domain filtering (for periodic noise)
- 27. V. IMAGE COMPRESSION Fundamentals Image Compression Models Elements of Information Theory Error-Free Compression Lossy Compression Image Compression standard 27
- 28. VI. IMAGE SEGMENTATION Detection of Discontiuties Edge Linking and Boundary Detection Thresholding Region-Oriented Segmentation Motion in Segmentation 28
- 29. VII. REPRESENTATION AND DESCRIPTION Representation Scheme Boundary Descriptors Regional Descriptors Morphology Relational Descriptors 29
- 30. VIII. RECOGNITION AND INTERPRETATION Elements of Image Analysis Patterns and Pattern Classes Decision-Theoretic Methods Structural Methods Interpretation 30

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