Adaptive color image enhancement based geometric mean filter

In this paper, an adaptive color image enhancement based on geometric mean filter is proposed. The contrast of the color image is enhanced by using saturation feedback from saturation components and incorporating spatial information into luminance components. Hue is preserved in order to avoid color distortion. The adaptive luminance enhancement is achieved by using a geometric mean filter in place of arithmetic mean filter since arithmetic mean filter tends to lose image detail such as edges and sharpness when compared to geometric mean filter. The traditional algorithm uses the arithmetic mean filter which smoothes local variations of luminance and saturation. The reconstructed quality of image using this scheme is generally not satisfactory. In the proposed method, geometric mean filter has been adopted that achieves very good quality reconstructed images, far better than that possible with the arithmetic mean filter. It not only enhances poor quality images but also solves the problem of gray world violation. The experimental results show that color images enhanced by this algorithm are clearer, vivid and efficient.