On the evaluation of illumination compensation algorithms

This paper presents a comparison framework for algorithms that can diminish the effects of illumination in images. Its main objective is to reveal the positive and negative characteristics of such algorithms, allowing researchers to select the most appropriate one for their target application. The proposed framework utilizes artificial illumination degradations on real images, which are then processed by the tested algorithms. The results are evaluated by an ensemble of performance metrics, highlighting the various characteristics of the algorithms across a range of different image attributes. The proposed framework represents a useful tool for the selection of illumination compensation algorithms due to a) its quantitative nature, b) its multifaceted analysis and c) its easy reproducibility. The validity of the proposed framework is tested by applying it to the enhancement results of four illumination compensation algorithms, which are used as preprocessing in two classic computer vision applications. The improvements brought about by the algorithms are in accordance with the predictions of the proposed framework.