Performance Evaluation of Filters for Enhancement of Images in Different Application Areas

Contrast stretching is a method to enhance the image details. Histogram equalization is one of the most common spatial domain technique used for contrast enhancement. While stretching the contrast of an image , it results in the degradation of brightness of the image. To overcome this hurdle a method known as Bi- Histogram equalization was introduced to preserve the brightness level of the output image. As the contrast enhancement enhances the image , it also enhances the noise in the signal that causes the blurrness in the output image. The proposed BHEGF method reduces this drawback and provides more accurate and realistic results. In this paper we use four different types of filters i.e. median filter , gaussian filter , average filter & motion filter and various parameters such as processing time , MSE, brightness count and PSNR are used for performance evaluation. Keywords: Bi- Histogram Equalization , Tri- histogram equalization ,BHEGF , Contrast enhancement , Gaussian filter , brightness preserving , Spatial and frequency domain. I. Introduction The main purpose of image enhancement is to enhance or improve the quality of image with the minimum amount of MSE. Various enhancement techniques are used for this purpose both in spatial domain and frequency domain. This paper emphasizes mainly on spatial domain technique of image enhancement. The histogram equalization is the most commonly used technique for contrast enhancement and brightness preservation of the image. There are various techniques used to equalizes the given image's histogram such as BHE, DSHE, AHE, CHE, MHE, MMBEBHE, RMSHE. In all these technique the image have to first decomposed and then the histogram of each part generated from the decomposition is equalized . That is also known as the segmentation of an image into various sub-images. Segmentation of the image can be done in various ways such as amplitude thresholding, component labeling, boundary based approaches, region based approaches, template matching, texture based segmentation. In this paper, the image is segmentized on the bases of its component levels. Then the resultant histograms of the sub-images are equalized, while equalizing the histograms the impulse noises present in the images are also get enhanced along with the enhancement of the image (4) . Hence to overcome this drawback due to noise in the satellite imaging, this paper proposed the use of the Gaussian filter to filter out the noise from the enhanced image . The use of Gaussian filter shell reduced the MSE to a larger extent from the enhanced image . 1.1 Image Enhancement Image enhancement is a process of improving the quality of image by improving its features. The techniques used for image enhancement can be used to improve the image's contrast and brightness characteristics and reduces its noise contents or sharpen its details they can be classified as subjective enhancement and objective enhancement (9). Subjective enhancement techniques may be repeatedly applied in various forms until the observer feels that the image yields the details necessary for particular application. On the other hand objective image enhancement corrects an image for known degradation. This enhancement is not repeatedly applied but it is applied only once based on the measurement taken from the system. Image enhancement can also be categorized into two main and broad categories. They are as follows: