Digital Image Contrast Enhancement using Contrast-Tone Optimization Method

Image enhancement is the process of manipulating an image so that the result is more suitable than the original for a specific application. A very popular technique for image contrast enhancement is histogram equalization (HE). This technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. However, Histogram equalization (HE) results in excessive contrast enhancement gives unnatural look to the processed image and creates visual artifacts such as ringing and magnifying noises. Many other image contrast enhancement techniques such as adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE) were proposed. The main drawback of these techniques is that the tone continuity of the original image could not be preserved. This paper proposes a new contrast-tone optimization technique which enhances the contrast of an image while meantime preserving the tone continuity of the processed image. In this paper we formulate contrast enhancement as a problem of maximizing contrast gain subject to a limit on tone distortion and possibly other constraints such as intensity level of output image that suppress visual artifacts. The resulting contrast-tone optimization problem can be solved efficiently by linear programming. The proposed technique can optimize the transfer function such that sharp contrast and subtle tone are best balanced according to application requirements and user preferences. This technique offers a greater and more precise control of visual effects than existing techniques of contrast enhancement.