DOI:10.23883/IJRTER.2020.6049.V2AAF 22 CONTRAST IMAGE ENHANCEMENT USING LUMINANCE COMPONENT BASED ON WAVELET TRANSFORM Ronak Sharma 1 , Ajay Kumar Yadav 2 1,2 Mewar University Rajsthan Abstract- The goal of Contrast image enhancement is to improve the image quality so that the resultant image is better than the original image for a specific application or set of objectives. Image enhancement refers to accentuation or sharpening of image features such as edges, boundaries or contrast to make a graphic display more useful for display and analysis. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen features so that they can be detected easily. There are many image enhancement techniques to obtain satisfactory result, but here I use color image enhancement and I got good results. I. INTRODUCTION Image enhancement Techniques The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. Image enhancement techniques can be divided into two broad categories: 1. Spatial domain methods, which operate directly on pixels, and 2. Frequency domain methods, which operate on the Fourier transform of an image. Unfortunately, there is no general theory for determining what is `good' image enhancement when it comes to human perception. If it looks good, it is good! However, when image enhancement techniques are used as pre-processing tools for other image processing techniques, then quantitative measures can determine which techniques are most appropriate. Grey scale manipulation The simplest form of operation is when the operator T acts only on a 1 x 1 pixel neighborhood in the input image, that is ^ F(x; y) depends on the value of F only at (x; y). This is a grey scale transformation or mapping. The simplest case is thresholding where the intensity profile is replaced by a step function, active at a chosen threshold value. In this case any pixel with a grey level below the threshold in the input image gets mapped to 0 in the output image. Other pixels are mapped to 255 Other grey scale transformations are outlined in Figure 1 Below