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Unformatted text preview: Purdue University: ECE438 - Digital Signal Processing with Applications 1 ECE438 - Laboratory 10: Image Processing (Week 1) October 6, 2010 1 Introduction This is the first part of a two week experiment in image processing. During this week, we will cover the fundamentals of digital monochrome images, intensity histograms, pointwise transformations, gamma correction, and image enhancement based on filtering. In the second week , we will cover some fundamental concepts of color images. This will include a brief description on how humans perceive color, followed by descriptions of two standard color spaces . The second week will also discuss an application known as image halftoning . 2 Introduction to Monochrome Images An image is the optical representation of objects illuminated by a light source. Since we want to process images using a computer, we represent them as functions of discrete spatial variables. For monochrome (black-and-white) images, a scalar function f ( i,j ) can be used to represent the light intensity at each spatial coordinate ( i,j ). Figure 1 illustrates the convention we will use for spatial coordinates to represent images. If we assume the coordinates to be a set of positive integers, for example i = 1 ,...,M and j = 1 ,...,N , then an image can be conveniently represented by a matrix. f ( i,j ) = f (1 , 1) f (1 , 2) f (1 ,N ) f (2 , 1) f (2 , 2) f (2 ,N ) . . . . . . . . . f ( M, 1) f ( M, 2) f ( M,N ) (1) We call this an M N image, and the elements of the matrix are known as pixels . The pixels in digital images usually take on integer values in the finite range, f ( i,j ) L max (2) Questions or comments concerning this laboratory should be directed to Prof. Charles A. Bouman, School of Electrical and Computer Engineering, Purdue University, West Lafayette IN 47907; (765) 494- 0340; email@example.com Purdue University: ECE438 - Digital Signal Processing with Applications 2 origin f(i,j) i j Figure 1: Spatial coordinates used in digital image representation. where 0 represents the minimum intensity level (black), and L max is the maximum intensity level (white) that the digital image can take on. The interval [0 ,L max ] is known as a gray scale . In this lab, we will concentrate on 8-bit images, meaning that each pixel is represented by a single byte. Since a byte can take on 256 distinct values, L max is 255 for an 8-bit image. 2.1 Exercise Download yacht.tif Help on image command In order to process images within Matlab, we need to first understand their numerical representation. Download the image file yacht.tif . This is an 8-bit monochrome image....
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