Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab

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Thank you for your interest You will be notified when this product will be in stock. I agree to the. Exponential transform 3. Power-law gamma transform 3. Application: gamma correction 3. Pixel distributions: histograms 3. Histograms for threshold selection 3. Adaptive thresholding 3.

Contrast stretching 3. Histogram equalization 3. Histogram equalization theory 3. Histogram equalization theory: discrete case 3. Histogram equalization in practice 3. Histogram matching 3.


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  • FUNDAMENTALS OF DIGITAL IMAGE PROCESSING: A PRACTICAL APPROACH WITH EXAMPLES IN MATLAB | Van Schaik.
  • Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab.

Histogram-matching theory 3. Histogram-matching theory: discrete case 3. Histogram matching in practice 3. Adaptive histogram equalization 3.


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  5. Histogram operations on colour images Exercises 4. Enhancement 4. Why perform enhancement? Enhancement via image filtering 4. Pixel neighbourhoods 4. FiLter kernels and the mechanics of linear filtering 4. Nonlinear spatial filtering 4. Filtering for noise removal 4. Mean filtering 4. Median filtering 4. Rank filtering 4. Gaussian filtering 4. Filtering for edge detection 4. Derivative filters for discontinuities 4.

    ISBN 13: 9780470844731

    First-order edge detection 4. Linearly separable filtering 4. Second-order edge detection 4. Laplacian edge detection 4. Laplacian of Gaussian 4. Zero-crossing detector 4. Edge enhancement 4.

    MATLAB tutorial: Image Processing Basic (6 functions in 4 mins)

    Laplacian edge sharpening 4. Fourier transforms and frequency-domain processing 5. Frequency space: a friendly introduction 5. Frequency space: the fundamental idea 5. Fourier series 5. Calculation of the Fourier spectrum 5. CompLex Fourier series 5. Understanding the Fourier transform: frequency-space filtering 5. Linear systems and Fourier transforms 5.

    Digital Fourier transforms: the discrete fast Fourier transform 5. Sampled data: the discrete Fourier transform 5. Image restoration 6. Imaging models 6. Nature of the point-spread function and noise 6. Restoration by the inverse Fourier filter 6. Wiener-Helstrom Filter 6. Origin of the Wiener-Helstrom filter 6. Acceptable solutions to the imaging equation 6.

    Constrained deconvolution 6. Estimating an unknown point-spread function or optical transfer function 6. Blind deconvolution 6. Iterative deconvolution and the Lucy-Richardson algorithm 6. Matrix formulation of image restoration 6. Constrained least-squares restoration 6.

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