A Naive Bayes approach to improve histogram matching quality

Abstract

Histogram Matching (HM) is a well known technique for matching the colors of a source image to those of a reference image. A major application is High Dynamic Range Imaging, where images with different exposures have to be color matched. HM works best when the images are aligned and the color difference is small. However, due to camera or object motion, the images may not be aligned, which can result in color artifacts. Furthermore, HM typically involves brightening dark regions of the source image, which increases the image noise. As a result, there is a need for detecting and correcting color artifacts and increased image noise. In this paper we present a novel post-processing method for improving the quality of histogram matched images using statistical properties of the reference image. The proposed method is based on ensembles of neighboring pixels. We learn the statistics of such ensembles within the reference image (target image), which exhibits content difference in comparison to the source image. Based on these statistics, we detect and reconstruct faulty intensities, by following a naive Bayes approach. In addition to its relative easiness of implementation, experimental results show considerable subjective (visual) as well as objective (PSNR) improvements over the original HM. Moreover, promising results are obtained on comparisons of our method with Non-Local-Means denoising based post-processing, which is a state-of-the-art denoising method.