A pre-processing approach for efficient feature matching process in extreme illumination scenario

Video or image enhancement is a crucial part in image processing field as it improves the quality of the image before any further processes is applied on the image, which includes feature matching. In this paper, the accuracy of SURF feature descriptors used in feature matching between two input images of extreme illumination levels are evaluated. Based on the evaluation results, a novel pre-processing method to equalize both images intensity with respect to each other while maintaining the image content is proposed. We do so by fusing the cumulative histogram of the input images to compute a new cumulative histogram that will be used to remap both images. From this simple method, the results show that the intensity levels of the images are equalized and accuracy of the feature matching process is improved, in the event of extreme illumination scenario.