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Learning materials: "Digital Image Processing MATLAB Edition" (second edition) Gonzalez
Article Directory
Learning Catalog
The first chapter, "Introduction"
- Getting started concept
- Image input, output, and display (imread / imshow / imwrite / size / whos)
- Class and image type (data type / binary image / islogical / im2uint8 / mat2gray)
- Programming function M: M files and operators
- Programming function M: flow control and array, matrix, logical index
- Programming M functions: function handle, cell array, structures and code optimization (tic / toc / timeit)
Chapter "gradation conversion and spatial filtering"
- Outline
- Gradation conversion function: imadjust / imcomplement / stretchlim
- Gradation conversion functions: the logarithm and contrast stretching transformation (g = c * log (1 + f))
- Gradation conversion function: Specify any other gradation conversion and gradation conversion function for M (intrans / gscale / nargin / nargout / nargchk / varargin / varargout / imterpl)
- Histogram processing and plotted as a function of: generating a histogram and plotted (imhist / bar / stem / plot / fplot)
- Histogram processing and plotted as a function: Histogram equalization (function histeq / function cumsum)
- Histogram processing and graphics functions: histogram matching (function histeq)
- Histogram processing and plotted as a function: contrast limited adaptive histogram equalization function adapthistteq
- Spatial filtering: linear spatial filtering (IMFilter function)
- Spatial filtering: linear spatial filtering (function colfilt / padarray /)
- Spatial filtering: linear spatial filter (fspecial / imfilter)
- Spatial filtering: nonlinear spatial filter (ordfilt2 / medfilt2)
Chapter 3, "Frequency domain filtering"
- Two-dimensional discrete Fourier transform
- Observation and calculation in MATLAB dimensional DFT (fft2 / abs / fftshift / ifftshift / ifft2 / real / angle / atan2)
- Frequency domain filtering: base
- Frequency domain filtering: filtering DFT basic steps
- Frequency domain filtering: M available function (function dftfilt)
- Filter to obtain frequency domain filter (freqz2) from the space
- Create achieve frequency domain filter grid array (dftuv)
- A low-pass (smoothing) filter in the frequency domain (the lpfilter)
- FIG wireframe and surface rendering (mesh / surf / meshgrid)
- The basic high-pass filter (function hpfilter)
- High-frequency emphasis filter
Chapter IV "image restoration and reconstruction."
- Image degradation / restoration processing model
- Using the noise-added image function imnoise
- Using a predetermined spatial distribution of the generated random noise (imnoise2)
- Periodic noise (imnoise3)
- Estimate the noise parameters (statmoments and roipoly)
- Spatial noise filter (spfilt)
- An adaptive spatial filter (adpmedia)
- Frequency domain filtering periodic noise reduction
- Degradation function modeling (pixeldup)
- Direct inverse filtering
- Wiener filter (deconvmnr / edgetaper)
- Image Reconstruction: theoretical knowledge
- Function radon / phantom / flipud
- Function iradon
- Process fan beam data (fanbeam / ifnbeam / fan2para / para2fan)
Chapter 5, "color image"
- RGB image (rgbcube)
- Index image (colormap / imapprox / whitebg)
- Handler index image and the RGB ()
- Color space conversion ({NTSC, YCbCr, HSV, CMY, and CMYK, HSI,} ice / interp1q / spline)
- The device-independent color space (makecform / applycform / repmat / iccread / cat)
- Color Image Processing
- Color conversion
- Smoothing a color image (component image extracting / rgb2hsi)
- Color image sharpening
- Edge detection using a gradient color (colorgrad)
- Image segmentation (colorseg / immultiply / reshape / find / diag) in RGB space vector
Chapter 6, "Image Compression"
- Overview background (imratio / whos / compare)
- Image compression coding redundancy (ntrop / hist / entropy)
- Huffman code (huffman / golabl / cell / sort / celldisp / cellplot)
- Huffman coding (mat2huff)
- Huffman decoding (code unresolved)
- Spatial redundancy (mat2lpc / lpc2mat /)
- Heart visual redundancy (quantize)
Chapter VII "image segmentation"
- Image Segmentation Overview
- Detection
- Line detection (pixeldup)
- Edge detection function using an edge (Sobel / LoG / Canny)
- Hough transform background
- Toolbox Hough function (hough / houghpeaks / houghlines)
- Threshold processing basics
- Substantially global thresholding (mean2 / im2bw)
- Method using Otsu thresholding optimal global (graythresh)
- Using image smoothing process to improve the global threshold
- Improved global using the edge threshold processing (percentile2i)
- Statistics based on local variable threshold processing (stdfilt / localmean / localthresh)
- Moving average image thresholding (movingthresh)
Extended Learning
- MATLAB installation
- MATLAB cracked version of the document needs to help solve the problem of license
- MATLAB shortcuts
- MATLAB: Run a "undefined function or variable"
- MATLAB: Undefined function or variable 'tofloat'.
- MATLAB: imshow (f) and imshow (f, []) difference
- MATLAB Matrix and Array
- In MATLAB
()
,[]
with{}
the difference and know - Commonly used in digital image processing MATLAB function
- Digital Image Processing: Glossary
- Digital Image Processing: Common Functions
- Image processing why sometimes need to be normalized?
- The reason for realization of linear spatial filtering image f zero padding?
- When the Fourier transform filtering, why the need for zero padding the input data?
- Fourier spectrum of the image display problems
- Zoom (calibration) issues when Fourier inverse transform
- In-depth understanding of - Laplace filter
- In-depth understanding of - time, frequency and spatial domain
- In-depth understanding of - Fourier Transform
- In-depth understanding of - convolution
- In-depth understanding of - image noise
- In-depth understanding of - image gradient
- Problems encountered knowledge