COMPARATIVE ANALYSIS FOR CONTRAST ENHANCEMENT USING HISTOGRAM EQUALIZATION TECHNIQUES

Jatinder Kaur; Onkar Ch

COMPARATIVE ANALYSIS FOR CONTRAST ENHANCEMENT USING HISTOGRAM EQUALIZATION TECHNIQUES

Jatinder Kaur^*1, Onkar Chand²

Electronics & Communication Engineering Department, Institute of Engineering & Technology Bhaddal, Ropar, India
Electronics & Communication Engineering Department, Institute of Engineering & Technology Bhaddal, Ropar, India

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Abstract

Contrast enhancement is basically improving the quality of images for better perception. They are widely used for image and video processing to achieve wider dynamic range. There are two major goals: contrast enhancement and preserving the brightness of the image. This paper presents the parameters comparison and simulation results of various histogram equalization techniques like GHE, BBHE, Novel, BPDHE in terms of Correlation, Peak Signal to Noise Ratio (PSNR), Normalized Absolute Error (NAE) due to which visual quality of the image becomes better.

Keywords

Histogram Equalization, Contrast Enhancement, GHE, BBHE, BPDHE, Novel

INTRODUCTION

Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values. It flattens and stretches the dynamic range of the image's histogram and resulting in overall contrast enhancement. Due to flattening property of HE, either it performs over or under enhancement. To avoid this problem, Kim in 1997 proposed Bi-level histogram equalization which preserves the mean brightness of a given image. In this algorithm, BBHE firstly divide an input image into two sub-images based on the mean of the input image. One of the sub-images is the set of samples less than or equal to the mean whereas the other one is the set of samples greater than the mean. Then the BBHE equalizes the sub-images independently based on their respective histogram which has an effect of preserving mean brightness [1].

Next, the mean brightness preserving histogram equalization (MBPHE) methods basically can be divided into two main groups, which are bisections MBPHE, and multi-sections MBPHE. Bisections MBPHE group is simplest group of MBPHE. Fundamentally, these methods are separate the histogram of two sections. These two histogram sections are then equalized independently. The major difference among the methods in this family is the criteria used to divide the input histogram [2]. Next, Dynamic Histogram equalization (DHE) technique, control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go through a repartitioning test to ensure the absence of any dominating portions.

This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects such as washed out appearance or undesired artefacts [3]. The brightness preserving dynamic histogram equalization (BPDHE) is actually an extension to both MPHEBP and DHE. Similar to MPHEBP, the method partitions the histogram based on the local maximums of the smoothed histogram. However, before the histogram equalization taking place, similar to DHE. As the change in the dynamic range will cause the change in mean brightness, the final step of this method involves the normalization of the output intensity. So it can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus BPDHE will produce better enhancement and fulfil the requirement of maintaining the mean brightness of the image [4].

In Novel method, Firstly applies some pre-processing steps on the histogram corresponding to the image and then applies histogram equalization [5].

PARAMETERS MEASURED

In order to test the proposed method, Simulation using Matlab7.10 are performed on input image. To evaluate the image enhancement performance, Peak Signal to Noise Ratio (PSNR), Normalized absolute error (NAE) and Correlation used as the criterion.

GLOBAL HISTOGRAM EQUALIZATION (GHE)

References

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