# Lecture Eight - Penn State College of Engineering

Lecture Eight Matlab for spatial filtering and intro to DFTs Figures from Gonzalez and Woods, Digital Image Processing, Copyright 2002, Gonzalez, Woods, and Eddins, Digital Image Processing with MATLAB, Copyright, 2004, and Jahne, Digital Image Processing, 4th Edition, Copyright, 1997

Imadjust g=imadjust(f,[low_in,high_in],[low_out,high_out],gamma) Includes various contract transformations. Chapter 3

Intensity Transformations and Spatial Filtering Logarithmic and Constrast Transformations

g=c*log(1+double(f)) gs=im2uint8(mat2gray(g)); % to range [0,1] and to gray scale Contrast transformation g=1./(1+(m./(double(f)+eps)).^E) Use of eps prevents overflow if f has any zero values

m is turning point Chapter 3 Intensity Transformations and Spatial Filtering

Stem and plot Find out syntax from typing help stem and help plot in MATLAB.

Chapter 3 Intensity Transformations and Spatial Filtering Histeq command

g=histeq(f,hspec) hspec is a specified histogram. If you do g=histeq(f,256) you get histogram equalization.

Chapter 3 Intensity Transformations and Spatial Filtering Chosen Histgram

p( z ) a1 2 1

2 1 exp( ( z 1 ) / 2 )

a2 2 2 exp( ( z 2 ) / 2 22 ) k

Values chosen a 1 1, a 2 0.07, 1 0.15, 2 0.75, 1 2 0.05 Two concentrated Gaussian peaks, one large, one small Chapter 3

Intensity Transformations and Spatial Filtering Spatial Filters g=imfilter(f,w,mode,bndry,size)

Mode= corr correlationstandard conv convolution, w rotated 180 degrees Chapter 3 Intensity Transformations

and Spatial Filtering Chapter 3 Intensity Transformations and Spatial Filtering

Chapter 3 Intensity Transformations and Spatial Filtering

Chapter 3 Intensity Transformations and Spatial Filtering Chapter 3

Intensity Transformations and Spatial Filtering Chapter 3 Intensity Transformations

and Spatial Filtering Fourier Transforms Based on notion Fourier introduced to Heat transfer that any periodic function can be written as a possibly infinite

sum of sines and cosines. Important in Differential equations Probability and statistics (characteristic functions, proof of central limit theorem) Almost any area of engineering you can name.

Chapter 4 Image Enhancement in the Frequency Domain Chapter 4

Image Enhancement in the Frequency Domain Chapter 4 Image Enhancement in the

Frequency Domain Chapter 4 Image Enhancement in the Frequency Domain

Chapter 4 Image Enhancement in the Frequency Domain

Chapter 4 Image Enhancement in the Frequency Domain Fourier Spectrum and Phase

Two images Mix up phase and amplitude Two pictures from another text

Mix up amplitude and phase Amplitude from 1, phase from 2, amplitude from 2, phase from one.

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