Abstract To avoid motion artefacts when merging multiple exposures into a high dynamic range image, a number of HDR deghosting algorithms have been proposed. However, these algorithms do not work equally well on all types of scenes, and some may even introduce additional artefacts. As the number of proposed deghosting methods is increasing rapidly, there is an immediate need to evaluate them and compare their results. Even though subjective methods of evaluation provide reliable means of testing, they are often cumbersome and need to be repeated for each new proposed method or even its slight modification. Because of that, there is a need for objective quality metrics that will provide automatic means of evaluation of HDR deghosting algorithms. In this work, we explore several computational approaches of quantitative evaluation of multi-exposure HDR deghosting algorithms and demonstrate their results on five state-of-the-art algorithms. In order to perform a comprehensive evaluation, a new dataset consisting of 36 scenes has been created, where each scene provides a different challenge for a deghosting algorithm. The quality of HDR images produced by deghosting method is measured in a subjective experiment and then evaluated using objective metrics. As this paper is an extension of our conference paper, we add one more objective quality metric, UDQM, as an additional metric in the evaluation. Furthermore, analysis of objective and subjective experiments is performed and explained more extensively in this work. By testing correlation between objective metric and subjective scores, the results show that from the tested metrics, that HDR-VDP-2 is the most reliable metric for evaluating HDR deghosting algorithms. The results also show that for most of the tested scenes, Sen et al.’s deghosting method outperforms other evaluated deghosting methods. The observations based on the obtained results can be used as a vital guide in the development of new HDR deghosting algorithms, which would be robust to a variety of scenes and could produce high quality results.

Various High Dynamic Range (HDR) deghosting algorithms have been developed to solve the problem of merging dynamic content in multi-exposure HDR imaging. Even though these algorithms may be successful in `ghost' removal, they may fail to reduce noise in the resultant HDR image. As a result, the presence of noise in the generated HDR image degrades the overall image quality. HDR deghosting algorithms should also aim to reconstruct values that are approximately proportional to the luminance of the real scene. In this work we evaluate noise and luminance reconstruction in HDR images generated by five state-of-the-art HDR deghosting algorithms. The observations based on the obtained results are instrumental to guide the development of new HDR deghosting algorithms that will also aim to reduce noise and reconstruct original scene luminance to produce a good quality deghosted HDR image.