A monotonic constrained regression framework for histogram equalization and specification

This paper introduces a general framework for image contrast enhancement based on histogram equalization (HE) and specification (HS). Traditional HE and HS are simple and effective, but they often amplify the noise level of the image while enhancing it. Furthermore, they may not utilize the entire dynamic range due to the discrete nature of the image. In our framework, image contrast enhancement is posed as a nonparametric monotonic constrained regression problem, in which both the two boundary values and the slopes of the brightness transform function are controlled. We show that such a framework provides an effective way to avoid enlarging the noise level and to utilize the entire dynamic range while performing HS (and also its special case HE). Our method can thus reduce the production of visual artifacts while enhancing the image.