Image Enhancement using Guided Filter for under Exposed Images

Image enhancement becomes an important step to improve the quality of image and change in the appearance of the image in such a way that either a human or a machine can fetch certain information from the image after a change. Due to low contrast images it becomes very difficult to get any information out of it. In today's digital world of imaging image enhancement is a very useful in various applications ranging from electronics printing to recognition. For highly underexposed region, intensity bin are present in darken region that's by such images lacks in saturation and suffers from low intensity. Power law transformation provides solution to this problem. It enhances the brightness so as image at least becomes visible. To modify the intensity level histogram equalization can be used. In this we can apply cumulative density function and probabilistic density function so as to divide the image into sub images. In proposed approach to provide betterment in results guided filter has been applied to images after equalization so that we can get better Entropy rate and Coefficient of correlation can be improved with previously available techniques. The guided filter is derived from local linear model. The guided filter computes the filtering output by considering the content of guidance image, which can be the image itself or other targeted image.