V. Nandhini ¹, R. Pratheepa ¹, N. Anjana ¹, V. Elamaran ¹

Affiliations
1 Department of Electronics and Communication Engineering, School of EEE, SASTRA University, Thanjavaur – 613401, Tamil Nadu, IN

Abstract

In the field of image processing, contrast enhancement is a vital area to enhance contrast of images which are having poor contrast. Histogram Equalization (HE) is the method used to increase contrast in images. To improve contrast in images several HE methods persist. By determining the Probability Density Function (PDF) and Cumulative Density Function (CDF), HE expands the distribution of pixels. The overall brightness would be altered while the histogram equalization is being applied is a one kind of disadvantage. The drawback which is mentioned here can be avoided by, classifying the image based on intensity exposure and is divided into sub images based on the median value. To minimize the over enhancement, the sub images are clipped using the threshold value. Equalization can be done separately for each sub images, and the equalized sub images are combined to form a single image. Thus, to keep the brightness and to bring limitation in enhancement rate the classification and subdivision based HE method was proposed which equalizes the image. For color images, this method of equalization performs better than gray scale images. The simulation results for several test images are obtained using Matlab software tool. The results show that the entropy of the proposed method is compared with the standard HE method and it determines the amount of information available in the image. The proposed method provides better enhancement than other methods of equalization by controlling the enhancement rate. Improvements can be done by selecting the threshold values for clipping and intensity exposure. Contrast enhancement is applied in the areas of photography, medical imaging and video surveillance systems to enhance quality in images and the image looks natural.

Keywords

Classifcation, Clipping, Contrast Enhancement, Entropy, Exposure, Histogram Equalization

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N. Anjana ¹, J. Jezebel Priestley ¹, V. Nandhini ¹, V. Elamaran ¹

Affiliations
1 Department of Electronics and Communication Engineering, School of EEE, SASTRA University, Thanjavaur – 613401, Tamil Nadu, IN

Abstract

Image enhancement is one of the major research areas in the field of digital image processing. The sole objective of this domain is to enhance the quality of a poor contrast image. As a result, the final processed image becomes much more understandable than the original one. A new edge based histogram equalization method is proposed in this study. A high pass filter is used to detect edges with a help of an appropriate gradient operator. In general, a convolution mask is used for this kind of area processes like filtering. This proposed method increases the quality of the poor contrast area. This method does not create an impact on brighter area in the given input image. A few portion of the poor contrast is raised and few others contrast are being reduced. The simulation results of this study are compared with the conventional histogram equalization. Our experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is better than the regular Histogram Equalization (HE) method. All simulation results are obtained using Matlab simulation software tool with standard color test images. Color image enhancement methods are applicable in the areas such as iris recognition, digital photography, remote sensing, biology, medicine, geophysics and microarray techniques, etc.

Keywords

Contrast Enhancement, Edge Detection, Histogram Equalization, Image Enhancement, PSNR

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