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Digital Image Processing. Image Enhancement (Histogram Processing). Come To The LABS!. Day: Wednesday Time: 9:00 – 11:00 Room: Aungier St. 1-005 We will start by getting to grips with the basics of Scilab Lab details available at WebCT

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Digital image processing l.jpg

Digital Image Processing

Image Enhancement

(Histogram Processing)


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Come To The LABS!

Day: Wednesday

Time: 9:00 – 11:00

Room: Aungier St. 1-005

We will start by getting to grips with the basics of Scilab

  • Lab details available at WebCT

    Shortly, there will be a Scilab assignment which will count towards your final mark


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Contents

Over the next few lectures we will look at image enhancement techniques working in the spatial domain:

  • What is image enhancement?

  • Different kinds of image enhancement

  • Histogram processing

  • Point processing

  • Neighbourhood operations


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A Note About Grey Levels

So far when we have spoken about image grey level values we have said they are in the range [0, 255]

  • Where 0 is black and 255 is white

    There is no reason why we have to use this range

  • The range [0,255] stems from display technologes

    For many of the image processing operations in this lecture grey levels are assumed to be given in the range [0.0, 1.0]


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What Is Image Enhancement?

Image enhancement is the process of making images more useful

The reasons for doing this include:

  • Highlighting interesting detail in images

  • Removing noise from images

  • Making images more visually appealing


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Image Enhancement Examples

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Image Enhancement Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Image Enhancement Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Image Enhancement Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Spatial & Frequency Domains

There are two broad categories of image enhancement techniques

  • Spatial domain techniques

    • Direct manipulation of image pixels

  • Frequency domain techniques

    • Manipulation of Fourier transform or wavelet transform of an image

      For the moment we will concentrate on techniques that operate in the spatial domain


Image histograms l.jpg
Image Histograms

Frequencies

Grey Levels

The histogram of an image shows us the distribution of grey levels in the image

Massively useful in image processing, especially in segmentation


Histogram examples l.jpg
Histogram Examples

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


Histogram examples cont l.jpg
Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Histogram Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

A selection of images and their histograms

Notice the relationships between the images and their histograms

Note that the high contrast image has the most evenly spaced histogram


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Contrast Stretching

We can fix images that have poor contrast by applying a pretty simple contrast specification

The interesting part is how do we decide on this transformation function?


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Histogram Equalisation

Spreading out the frequencies in an image (or equalising the image) is a simple way to improve dark or washed out images

The formula for histogram equalisation is given where

  • rk: input intensity

  • sk: processed intensity

  • k: the intensity range (e.g 0.0 – 1.0)

  • nj: the frequency of intensity j

  • n: the sum of all frequencies


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Equalisation Transformation Function

Images taken from Gonzalez & Woods, Digital Image Processing (2002)


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Equalisation Examples

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

1


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Equalisation Transformation Functions

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

The functions used to equalise the images in the previous example


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Equalisation Examples

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

2


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Equalisation Transformation Functions

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

The functions used to equalise the images in the previous example


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Equalisation Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

3

4


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Equalisation Examples (cont…)

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

3

4


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Equalisation Transformation Functions

Images taken from Gonzalez & Woods, Digital Image Processing (2002)

The functions used to equalise the images in the previous examples


Summary l.jpg
Summary

We have looked at:

  • Different kinds of image enhancement

  • Histograms

  • Histogram equalisation

    Next time we will start to look at point processing and some neighbourhood operations


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