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1、 1Lab 02: Image Enhancement Lab 02: Image Enhancement 19 March 2007 Part 1: Intensity and Histogram Manipulation 26 March 2007 Part 2: Spatial Filtering Report due: 02 April 2007 This lab comprises two parts and extends over two weeks. Intensity transformation and histo- gram manipulation are covere
2、d in Part 1, while spatial filtering is presented in Part 2. Given the coverage, two scenarios of work are suggested: (a) in the first tutorial, you may go through all examples in the two parts and ask questions along the way. In this case, you are required to work on the exercises on the next tutor
3、ial; (b) alternatively, the two parts can be followed se- quentially, one each week. In either case, the report for this lab is due on 02 April 2007. Unless approved by your tutor, you should do the lab (and the exercises) individually. The report for this lab is to be submitted one per student. Con
4、tents Contents 1 GETTING STARTED.2 1.1 IMAGE DATABASE.2 1.2 LECTURE NOTES.2 2 INTENSITY TRANSFORMATION.2 2.1 LINEAR CONTRAST STRETCHING.2 2.2 LOG AND POWER-LAW TRANSFORMATIONS.4 2.3 INTENSITY MAPPING BY LUT.5 2.4 EXERCISES.5 3 HISTOGRAM MANIPULATION .6 3.1 HISTOGRAM EQUALIZATION.6 3.2 HISTOGRAM SPEC
5、IFICATION.9 3.3 EXERCISES.9 4 PART 2: SPATIAL FILTERING.9 5 REFERENCES.9 21 1 Getting Started Getting Started Image enhancement techniques are used to process an image so that the result is more suitable than the original image for a particular application. Image enhancement can be defined objec- ti
6、vely such as by using the signal-to-noise-ratio or subjectively i.e. with respect to a particular problem and interpretation context. 1.11.1 Image Database Image Database For the works in this lab module, you will need to download the tut3-img.zip from the course website. 1.21.2 Lecture Notes Lectur
7、e Notes Perhaps after the break, you may want to skim through the lecture notes on Image Enhance- ment again before continuing. The notes can also be downloaded from the course website. 2 2 Intensity Transformation Intensity Transformation We use intensity transformation to map an images intensity v
8、alues to a new range. Given the spatial domain enhancement operation defined by: g(x) = Tf(x,y) where f(x,y) is the input image, g(x,y) is the output image, and T is an operator on f de- fined over a specified neighborhood of (x,y). We recall that the simplest form of the trans- formation T is when
9、the neighborhood is comprised only of a single pixel. In this case, the trans- formation depends only on the intensity value of the input pixel, thus essentially producing an intensity mapping operation. 2.12.1 Linear Contrast Stretching Linear Contrast Stretching Intensity mapping can be used to en
10、hance contrast. The idea is to stretch out the intensity val- ues to a wider range: ccdabafg+=)( where f and g are the input and output intensity values respectively; a to b is the original inten- sity range; and c to d is the stretched out range. In IPT, intensity mapping is supported by function i
11、madjust: g = imadjust(f,low_in high_in,low_out high_out) where low_in and high_in are the intensities in the input image which are mapped to low_out and high_out in the output image. For example, this code performs the stretching out of the rice.tif image: f = imread(rice.tif); g = imadjust(I,0.15 0.9,0 1); imshow(f), figure, imshow(g) Th
