"Digital Image Processing"

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Learning materials: "Digital Image Processing MATLAB Edition" (second edition) Gonzalez

Learning Catalog

The first chapter, "Introduction"

1. Getting started concept
2. Image input, output, and display (imread / imshow / imwrite / size / whos)
3. Class and image type (data type / binary image / islogical / im2uint8 / mat2gray)
4. Programming function M: M files and operators
5. Programming function M: flow control and array, matrix, logical index
6. Programming M functions: function handle, cell array, structures and code optimization (tic / toc / timeit)

Chapter "gradation conversion and spatial filtering"

1. Outline
2. Gradation conversion function: imadjust / imcomplement / stretchlim
3. Gradation conversion functions: the logarithm and contrast stretching transformation (g = c * log (1 + f))
4. Gradation conversion function: Specify any other gradation conversion and gradation conversion function for M (intrans / gscale / nargin / nargout / nargchk / varargin / varargout / imterpl)
5. Histogram processing and plotted as a function of: generating a histogram and plotted (imhist / bar / stem / plot / fplot)
6. Histogram processing and plotted as a function: Histogram equalization (function histeq / function cumsum)
7. Histogram processing and graphics functions: histogram matching (function histeq)
8. Histogram processing and plotted as a function: contrast limited adaptive histogram equalization function adapthistteq
9. Spatial filtering: linear spatial filtering (IMFilter function)
10. Spatial filtering: linear spatial filtering (function colfilt / padarray /)
11. Spatial filtering: linear spatial filter (fspecial / imfilter)
12. Spatial filtering: nonlinear spatial filter (ordfilt2 / medfilt2)

Chapter 3, "Frequency domain filtering"

1. Two-dimensional discrete Fourier transform
2. Observation and calculation in MATLAB dimensional DFT (fft2 / abs / fftshift / ifftshift / ifft2 / real / angle / atan2)
3. Frequency domain filtering: base
4. Frequency domain filtering: filtering DFT basic steps
5. Frequency domain filtering: M available function (function dftfilt)
6. Filter to obtain frequency domain filter (freqz2) from the space
7. Create achieve frequency domain filter grid array (dftuv)
8. A low-pass (smoothing) filter in the frequency domain (the lpfilter)
9. FIG wireframe and surface rendering (mesh / surf / meshgrid)
10. The basic high-pass filter (function hpfilter)
11. High-frequency emphasis filter

Chapter IV "image restoration and reconstruction."

1. Image degradation / restoration processing model
2. Using the noise-added image function imnoise
3. Using a predetermined spatial distribution of the generated random noise (imnoise2)
4. Periodic noise (imnoise3)
5. Estimate the noise parameters (statmoments and roipoly)
6. Spatial noise filter (spfilt)
7. An adaptive spatial filter (adpmedia)
8. Frequency domain filtering periodic noise reduction
9. Degradation function modeling (pixeldup)
10. Direct inverse filtering
11. Wiener filter (deconvmnr / edgetaper)
12. Image Reconstruction: theoretical knowledge
13. Function radon / phantom / flipud
14. Function iradon
15. Process fan beam data (fanbeam / ifnbeam / fan2para / para2fan)

Chapter 5, "color image"

1. RGB image (rgbcube)
2. Index image (colormap / imapprox / whitebg)
3. Handler index image and the RGB ()
4. Color space conversion ({NTSC, YCbCr, HSV, CMY, and CMYK, HSI,} ice / interp1q / spline)
5. The device-independent color space (makecform / applycform / repmat / iccread / cat)
6. Color Image Processing
7. Color conversion
8. Smoothing a color image (component image extracting / rgb2hsi)
9. Color image sharpening
10. Edge detection using a gradient color (colorgrad)
11. Image segmentation (colorseg / immultiply / reshape / find / diag) in RGB space vector

Chapter 6, "Image Compression"

1. Overview background (imratio / whos / compare)
2. Image compression coding redundancy (ntrop / hist / entropy)
3. Huffman code (huffman / golabl / cell / sort / celldisp / cellplot)
4. Huffman coding (mat2huff)
5. Huffman decoding (code unresolved)
6. Spatial redundancy (mat2lpc / lpc2mat /)
7. Heart visual redundancy (quantize)

Chapter VII "image segmentation"

1. Image Segmentation Overview
2. Detection
3. Line detection (pixeldup)
4. Edge detection function using an edge (Sobel / LoG / Canny)
5. Hough transform background
6. Toolbox Hough function (hough / houghpeaks / houghlines)
7. Threshold processing basics
8. Substantially global thresholding (mean2 / im2bw)
9. Method using Otsu thresholding optimal global (graythresh)
10. Using image smoothing process to improve the global threshold
11. Improved global using the edge threshold processing (percentile2i)
12. Statistics based on local variable threshold processing (stdfilt / localmean / localthresh)
13. Moving average image thresholding (movingthresh)

Extended Learning

1. MATLAB installation
2. MATLAB cracked version of the document needs to help solve the problem of license
3. MATLAB shortcuts
4. MATLAB: Run a "undefined function or variable"
5. MATLAB: Undefined function or variable 'tofloat'.
6. MATLAB: imshow (f) and imshow (f, []) difference
7. MATLAB Matrix and Array
8. In MATLAB (), []with {}the difference and know
9. Commonly used in digital image processing MATLAB function
10. Digital Image Processing: Glossary
11. Digital Image Processing: Common Functions
12. Image processing why sometimes need to be normalized?
13. The reason for realization of linear spatial filtering image f zero padding?
14. When the Fourier transform filtering, why the need for zero padding the input data?
15. Fourier spectrum of the image display problems
16. Zoom (calibration) issues when Fourier inverse transform
17. In-depth understanding of - Laplace filter
18. In-depth understanding of - time, frequency and spatial domain
19. In-depth understanding of - Fourier Transform
20. In-depth understanding of - convolution
21. In-depth understanding of - image noise
22. In-depth understanding of - image gradient
23. Problems encountered knowledge