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Abstract: An image is defined as an array, or a matrix, of square pixels arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and takeout several helpful information from it. This paper proposes a new method for efficient contrast enhancement of an image. In image processing low contrast image analysis is a challenging problem. Low contrast digital images reduce the ability of observer in analyzing the image. Histogram based techniques are used to enhance contrast of all type of medical images. The principal objective of image enhancement is to process a given image so that the result is more suitable than the original image for a specific application. The enhancement doesn't increase the inherent information content of the data, but it increases the dynamic range of the chosen features so that they can be detected easily. Here we propose a new method named “Modified Histogram Based Contrast Enhancement using Homomorphic Filtering" (MH-FIL) for medical images. In proposed method, we initially modify the histogram of input image using a histogram modification function and then we apply HE method for contrast enhancement on this modified histogram. After that we use homomorphic filtering for image sharpening and then to minimize the difference between input and processed image mean brightness, we normalize it.
Keywords: Contrast Enhancement, Histogram Equalization, Histogram Modification, Fourier Transformation , High Pass Filter, Homomorphic Filter.
I INTRODUCTION
In digital image processing contrast enhancement techniques are an important techniques for both human and computer vision. Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. Image enhancement means as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Whenever an image is converted from one form to another, some degradation occurs at the output. All such degradation can occurs while performing some operation such as digitizing, transmitting, scanning etc. Hence the output image has to undergo a process called image enhancement. In other words, contrast is the difference in visual properties that makes an object distinguishable from other objects and the background. In visual perception, contrast is determined by the difference in the colour and brightness of the object with other objects. Histogram based image enhancement is very popular technique to enhance contrast of an image. Histogram equalization is a method of contrast adjustment using the image's histogram. The aim of image enhancement is to provide `better' input for other automated image processing techniques. In HE we stretch high frequent intensities over high range of gray levels to achieve comparatively more flat histogram. This flattering causes the overall enhancement of contrast of the input image. So the image required further processing, in proposed paper histogram modification is used to overcome disadvantages of HE.
This paper is organized into 6 sections. Section 1 gives an overview of the paper. Section 2 gives a comparative analysis of the different contrast enhancement technique. Section 3 describes proposed method for enhancement contrast of an image. Conclusion is made in section 4.
Section 5 and section 6 cover the acknowledgement and references.
IMAGE ENHANCEMENT TECHNIQUE
Image enhancement technique can be divided into two broad categories:
Spatial based domain image enhancement :-
Spatial based domain image enhancement works directly on pixels. The main advantage of spatial based domain technique is that they are simple to understand and the complexity of these techniques is low which favours real time implementations.
Spatial domain methods can again be classified into two broad categories:
• Point Processing operation:
The simplest spatial domain operations occur when the neighborhood is simply the pixel itself. Used primarily for contrast enhancement.
• Spatial filter operations:
Filtering is used to modify or enhance an image. Spatial domain operation or filtering in which the processed value for the current pixel processed value for the current pixel depends on both itself and surrounding pixels. Hence Filtering is a neighborhood operation, in which the value of any given pixel in the output image is determined by applying some algorithm to the values of the pixels in the neighborhood of the input pixel.
Advantages: simple to understand and the complexity of these techniques is low which favours real time implementations.
Disadvantages: These techniques generally lacks in providing adequate and robustness requirements.
Frequency based domain image enhancement:-
Frequency based domain image enhancement is a term used to describe the analysis of mathematical functions with respect to frequency and operate directly on the transform coefficients of the image, such as Fourier transform, and discrete cosine transform (DCT). The basic idea in using this technique is to enhance the image by manipulating the transform coefficients.
Frequency domain methods can again be classified into three categories:
• Image Smoothing
• Image Sharpening
• Periodic Noise reduction by frequency domain filtering.
Advantages: low complexity of computations, ease of viewing and manipulating the frequency composition of the image and the easy applicability of special transformed domain properties.
Disadvantages: it cannot simultaneously enhance all parts of image very well and it is also difficult to automate the image enhancement procedure
PROPOSED METHOD
In proposed method, we initially modify the histogram of input image using a histogram modification function and then we apply HE method for contrast enhancement on this modified histogram. After that we use homomorphic filtering for image sharpening and then to minimize the difference between input and processed image mean brightness, we normalize it.
The input to the system is in the form of a digital image.
The proposed approach consist of following steps:
Input Image
Generation Of Histogram
Histogram Modification
Histogram Equalization.
Homomorphic Filtering.
Image Normalization
Algorithm:
Input: Accept digital image as input.
Output: Display the required image as the output.
Step 1: Accept the input image (in digital form).
Step 2: Generate histogram of input image.
Step 3: Perform histogram modification on input image.
a) Take the value of power low function.
b) Modify, histogram of input image using power low function.
Step 4: Perform homomorphic filtering on the output of step 3.
a) Use illumination-reflectance model
Step 5: Apply histogram equalization on output of Step 4.
Step 6: Perform image normalization.
Step 7: Enhanced image is displayed as output.
1. Generation of Histogram:
An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image.
The horizontal axis of the graph represents the tonal variations, while the vertical axis represents the number of pixels in that particular tone. The left side of the horizontal axis represents the black and dark areas, the middle represents medium grey and the right hand side represents light and pure white areas. The vertical axis represents the size of the area that is captured in each one of these zones. Histograms plots how many times gray level occurs. Before discussing the use of However, an image histogram, shows frequency of pixels intensity values.
Histogram Equalization (HE)
One of the most popular global contrast enhancement techniques is histogram equalization (HE). Histogram equalization is the technique by which the dynamic range of the histogram of an image is increased. HE assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities. It improves contrast and the goal of HE is to obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image.
Let us denote the inverse transformation by r = T -1(s). We assume that the inverse transformation also satisfies the above two conditions.
We consider the gray values in the input image and output image as random variables in the interval [0, 1].
Let pin® and pout(s) denote the probability density of the Gray values in the input and output images.
If pin® and T® are known and r = T -1(s) satisfies condition 1, we can write (result from probability theory):
(4.4)
One way to enhance the image is to design a transformation T(.) such that the gray values in the output is uniformly distributed in [0, 1], i.e. pout (s) = 1, 0 £ s £1
In terms of histograms, the output image will have all gray values in “equal proportion” . This technique is called histogram equalization.
Next we derive the gray values in the output is uniformly distributed in [0, 1].
Consider the transformation
(4.5)
Note that this is the cumulative distribution function (CDF) of pin ® and satisfies the previous two conditions. From the previous equation and using the fundamental theorem of calculus,
Therefore, the output histogram is given by
(4.6)
The output probability density function is uniform, regardless of the input.
Thus, using a transformation function equal to the CDF of input gray values r, we can obtain an image with uniform gray values. This usually results in an enhanced image, with an increase in the dynamic range of pixel values.
Advantages
• Simple and less complex
Disadvantages
• The brightness of an image is changed after the histogram equalization.
• It may increase the contrast of background noise.
5. Image Normalization:
In image processing, normalization is a process that changes the range of pixel intensity values. Applications include photographs with poor contrast due to glare, for example. Normalization is sometimes called contrast stretching or histogram stretching. In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization. Often, the motivation is to achieve consistency in dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue.
IV Conclusions
Image enhancement and information extraction are two important components of digital image processing. Image enhancement techniques help in improving the visibility of any portion or feature of the image suppressing the information in other portions or features. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image.