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CHAPTER 1

INTRODUCTION

1.1 GENERAL

Digital image processing is a technique used to analyse the required

particular part of the images which comprises two levels of processing. Low-

level methods usually use very little knowledge about content of images.

These methods often include image compression, pre-processing methods for

noise filtering, edge extraction and image sharpening. High-level processing

is based on knowledge, goals and plans of how to achieve those goals, and

artificial intelligence methods are widely applicable (Milan Sonka et al 1993).

Low-level processing helps to enhance high-level processing. These

image processing techniques help to transfer the image from one form to

another form. The input image may undergo a wide range of enhancement

processes such as filters, contrast stretching, brightness, colour adjustments,

etc., to extract the required information from the image.

The direct human visualization of the images may lead to providing

better analysis of high visual images. In the case of poor visual images those

who have poor contrast and poor brightness it is difficult to analyse specific

parts of the images. Enhancement of these images provides more flexibility in

handling the images.

In the case of poor visual images poor contrast and brightness

images make more difficult to review the particular part of the image.

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Enhancement of images provides more flexibility in handling the images.

Enhancement techniques are widely used in real time applications such as

consumer electronics, medical image and disease analysis, cloud image

analysis, space image analysis, defect detection in the processing industries,

biometric security authentications and various other applications.

1.2 NEED FOR CONTRAST ENHANCEMENT

Better image quality is preferred for the analysis of any image. In

many forms of imaging devices, the quality of images is often affected by a

variety of factors including distortion and noise produced due to lack of

experience in taking images by the operator and adverse environmental

conditions, such as unfavourable illumination. As a result, the captured

images may not reveal sufficient details of the true scene and, even worse

which may contain artifacts such as washed-out and unnatural appearances. In

these cases, Contrast Enhancement (CE) techniques are useful to produce

more visually pleasing and informative images.

1.3 IMAGE ENHANCEMENT

The principle objective of Image Enhancement (IE) is to process a

given image so that the result is more suitable than the original image for an

exact application. Image enhancement is one of the most important issues in

low-level image processing by William Pratt (2007). Its purpose is to improve

the quality of low contrast images, i.e., to enhance the intensity difference

among objects and background. IE is an active research area for many

decades. Most of the studies are intended to produce better image quality with

improved interpretability by altering the original input image.

The aspire is to improve the visual appearance of the image, or to

provide a “better” transform representation for future automated image

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processing, such as analysis, detection, segmentation and recognition. Thus, a

considerable amount of research has focused on this subject, and numerous

enhancement techniques have been developed by Ming Zeng et al (2012).

Moreover, it helps to analyze background information that is essential to

understand object behaviour without requiring expensive human visual

inspection.

A good number of methods have been developed and they can

mainly be divided into two classes: local and global methods. Local methods

employ feature-based approaches and the local features can be gained by

using edge operators or by computing local statistics such as local mean,

standard deviation, etc. They are common contrast enhancement by

modifying the features. The common feature-based method is to define the

contrast first and enhance image contrast by increasing the contrast ratio.

Another method uses local histogram modification to enhance image contrast

in a local area, such as (1) local histogram equalization (2) local histogram

stretching and (3) nonlinear mapping methods (square, exponential, and

logarithmic function).

These methods are quite useful in local texture enhancement.

However, they may distort original images since the transformation is not a

monotonic mapping and the order of gray levels of the original image may be

changed significantly. Global methods are mainly implemented by using

histogram modification approaches. One of the most commonly used methods

is Histogram Equalization (HE).

The main idea of HE-based methods is to re-assign the intensity

values of pixels to make the intensity distribution uniform to the utmost

extent. Suppose that the original image is normalized and the range of its

intensities is [0, 1] and p(x) is the density function of intensity distribution of

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the original image, where x denotes the intensity value of the normalized

image. The desired density function of intensity distribution of the output

image is equal to one after equalization.

Histogram based IE algorithms are most widely used algorithms for

image enhancement. However, these algorithms may over enhance some

bright regions, which may affect the application of these enhanced images.

Most of them are mainly used to restore images from noise environment.

Frequency and wavelet domain based algorithms are also efficient ways for

IE in some cases. Image features that should be used in these algorithms are,

usually not easy to be constructed.

1.4 SPATIAL DOMAIN AND FREQUENCY DOMAIN

ENHANCEMENT

Many techniques exist that can enhance a digital image without

degrading it. IE approaches fall into two broad categories, spatial domain and

frequency domain method. Figure 1.1 shows a block diagram for spatial

domain enhancement technique. It considers image as a matrix.

Figure 1.1 Spatial domain approach

The term spatial domain refers to the image plane itself and

approaches in this category are based on direct manipulation of pixels in an

image. Spatial domain IE method is based on direct manipulation of pixels in

an image. It deals with spatial frequency, i.e. difference between the highest

and lowest values of a contiguous set of pixels. The main advantage of this

technique is that they are conceptually simple to understand, and the

ProcessingInput Image Output Image

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complexities of these techniques are low which favours real-time

implementations.

Block diagram of image enhancement in frequency domain is shown

in Figure 1.2, which represents an image as a weighted sum of sinusoidal

functions.

Figure 1.2 Frequency domain approach

Frequency domain processing techniques are based on modifying

the Fourier transform of an image. Frequency domain based IE is a term used

to describe the analysis of mathematical functions or signals with respect to

frequency and operate directly on the transform coefficients of the image,

such as Fourier Transform (FT), Discrete Wavelet Transform (DWT) and

Discrete Cosine Transform (DCT). The technique enhances the image by

manipulating the transform coefficients.

1.5 CONTRAST ENHANCEMENT

The difference in light intensity between the image and the adjacent

background relative to the overall background intensity is called contrast.

Contrast in black and white images is, usually among one of the three labels

i.e. high, low and normal. It is usual that the human eye can distinguish

between the images with a minimal contrast value of 0.02 (2 percent). Low

contrast images that are often described as flat or soft do not have highlights

or shadows but only the shades of gray without much variation between the

one and the other. On the other hand, high contrast images consist of black

and white with few gray levels.

Frequency

distribution

Input Image Output Image

ProcessingInverse

Transformation

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Contrast Enhancement (CE), as it is believed, is a fundamental step

in image segmentation, is the most important in image processing and

analysis and is used to transform an image on the basis of psychophysical

characteristics of human visual system. The techniques of CE have two

categories viz., direct methods of contrast enhancement, and indirect methods

of contrast enhancement. Using the direct approach while modifying a

contrast image, the intensities occupy small portions of available intensity

range. However, the original gray level has a new value after histogram

modification, and the result is the expansion of intensity span of pixels. The

two popular indirect contrast enhancement methods are histogram

specification and histogram equalizations.

Since the histogram modification stretches the global distribution of

the intensity, the modification of intensity distribution inside small regions of

the image should be done to fit an image to the human eye. However, the

direct contrast enhancement method establishes and increases the image and

criterion of contrast measurement.

In practice, almost all the most popular contrast enhancement

methods fall into the first category. Further, indirect methods are classified

into two sub-categories/classes – histogram modification technique (Spatial

domain technique) and transform domain technique. Researchers have paid

attention to histogram modification techniques since it is simple and efficient

as contrast can be measured locally and globally. But global histogram

modification techniques attempt to modify a spatial histogram of an image so

as to closely match an uniform distribution through the transform function

and it thus results in a limitation in the contrast enhancement in some parts of

the image.

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A direct extension of global histogram modification is called local

histogram modification, but it causes over-enhancement and noise enhancing

artifacts in some parts of the image. So as to improve the visibility of the

small details efficiently, local histogram modification techniques equalize

each histogram on the basis of histogram separation. A good contrast

enhancement technique must specifically address significant properties like

noise tolerance, uniform contrast, brightness preservation and convenient

implementation.

Histogram equalization is one of the well-known method that is

useful in various applications of image processing. Histogram equalization

flats the density distribution and enhances the contrast of the image as HE has

effect on stretching dynamic range. Even though, it has high performance

while enhancing the contrasts of a given image, it is rarely employed because

the direct use of the histogram improves brightness of the input image and

visual quality but introduces artifacts.

CE has wide application in digital photography, industry, LCD display,

medical image analysis, digital multimedia systems, radiography, arterial

visualization of medical imaging, tumor microcirculation, restoration of ancient

paintings, the vision impairment estimation, recovery of underwater visibility,

bas-relief generation, face recognition and radar image processing. When the

contrast information is not known, the processing of high contrast images leads

to degradation and reduction of the quality of the images.

It is the CE that improves the perceptibility of objects by enhancing

the brightness difference between the objects and their background, but it is

determined by its dynamic range which is the ratio between the brightest and

darkest pixel intensities. Various improper contrast images exist, and it is the

time of the day, duration of the exposure, fog and reflected light that affect the

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images. There are different contrast enhancement algorithms available to

obtain better contrast image from the improper. As some metrics does not suit

with the perpetual contrast, the task is on to find a suitable metric to analyze

various enhancement algorithms.

1.6 HISTOGRAM EQUALIZATION

Histogram equalization is relatively a simple and popular technique

for image enhancement and improves the quality of images that has poor

lighting. HE transforms the gray levels of an image to an uniform histogram

based on the occurrence of gray levels in the input images. Although it is

widely accepted and used in the fields of radar and medical image processing,

it is not common in consumer electronics as it can cause excessive brightness

saturation.

In the past, to overcome this problem, variants of HE based

techniques have been developed which can be classified into two categories.

(1) Automatic i.e. the process does not require user intervention. (2)

Adjustable – here the user must adjust the parameter to regulate the degree of

enhancement. It is necessary that an ideal contrast enhancement technique for

consumer electronics should be able to enhance images contrast without any

annoying distortions. It lacks adjustment mechanism to control the level of

the enhancement and cannot make satisfying balance on the details between

bright parts and dark parts.

It may over enhance or generate excessive noise to the image in

certain applications.

It may sometimes dramatically change the average brightness

of the image.

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1.7 MOTIVATION

Photographic images captured under low illumination may result

into a low contrast image. CE on images without contrast information may

lead to degradation of good quality images. Therefore, it requires an

automated algorithm to identify the contrast level of the image before

processing. The high contrast images need not be processed. However, the

low contrast images need to be enhanced further for perfect visualization and

analysis.

1.8 OBJECTIVES OF THESIS WORK

Various types of algorithms are developed for the CE of different

histogram shapes. The major objectives of this work are

To classify the input image contrast as low or high, so as to

ascertain whether enhancement is required or not.

To identify the type of histogram present in the image to

choose the respective contrast enhancement algorithms. And to

develop an algorithm for multi-peak histogram equalization for

low contrast images.

To develop a CE algorithm for images having skewed input

histogram and analyze the CE rate.

To develop an algorithm for CE of Gaussian peak histogram

for steel microstructure images. To validate this, an attempt is

made for analyzing the contrast of real-time microstructure

images.

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1.9 SCOPE OF THE THESIS

Histogram based techniques are preferred for their popularity,

performance and computational complexity. The main focus of this thesis

is to discuss the drawbacks of conventional histogram equalization

methods and to propose improved algorithms for contrast enhancement.

The overall block diagram is shown Figure 1.3. First the thesis contributes

to classify the images as low and high contrast images. After this, the

different histogram shapes like Multi peak, Skewed and Gaussians patterns

are classified.

Figure 1.3 Overall block diagram

Histogram Modeling and Classification

Low Contrast Image High Contrast Image

Pixel Intensity Histogram

Classification

Modified Contrast

Enhancement for Multipeak

Histogram Equalization

Adaptive Contrast

Enhancement for Skewed

Histogram

Enhanced Gaussian

Like Histogram Images

Contrast Enhanced output image

Input Image

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Then three different types of global CE techniques are proposed

for improving the image contrast. These techniques have been implemented

with different image sets and results were compared according to the

objective quality metrics. The thesis focuses mainly on CE techniques for the

study, implementation and comparison. With the proposed algorithm, the

quality of images has been improved and the outputs will be useful for further

image processing.

1.10 ORGANIZATION OF THE THESIS

The thesis is organized in seven chapters. Introduction on image

processing, image enhancement, contrast enhancement, shapes of histograms,

need, scope and objectives are discussed in Chapter 1.

Chapter 2 discusses the related works of various scientists and

researchers working in the area of image enhancement, contrast enhancement,

partitioned histogram equalization, histogram modification, contrast

classification and other various parameters.

Chapter 3 deals with the contrast classification of an image such as

low contrast and high contrast of the images. Chapter 4 provides a detailed

discussion and analysis of the octagon based histogram equalization for image

contrast enhancement through histogram modification. The results were

compared with contemporary methods.

Chapter 5 provides a controlled contrast enhancement using

Modified Histogram Equalization technique. The partition based upon the

median value with controlling enhancement rate parameter. Related work is

described, and different parameters metrics used to analyze the outputs.

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Chapter 6 shows the proposed brightness preserving histogram

equalization method to enhance steel metallographic images based on

neighbourhood technique. The output results are analyzed in Gaussian

histogram patterns. Finally, Chapter 7 presents final considerations and

concluding remarks. Future works and directions are pointed as well.

1.11 CONCLUSION

This chapter has discussed the importance of histogram equalization

in digital image processing, needs for contrast enhancement in the spatial and

frequency domains and the motivation of the research work. The main

objective of the thesis and the corresponding sub-classification were

enlightened. An introduction to the four proposed contrast enhancement

techniques was given. The scope of the thesis was explained with the

drawback of existing methods. Finally, the organization of the thesis was

elaborated.