Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging

High dynamic range (HDR) images can be generated by capturing a sequence of low dynamic range (LDR) images of the same scene with different exposures and then merging those images to create an HDR image. During capturing of LDR images, any changes in the scene or slightest camera movement results in ghost artifacts in the resultant HDR image. Over the past few years many algorithms have been proposed to produce ghost free HDR images of dynamic scenes. In this study we performed subjective psychophysical experiments to evaluate four algorithms for removing ghost artifacts in the final HDR image. To our best knowledge, no evaluation of deghosting algorithms for HDR imaging has been published. Thus, the aim of this paper is not only to evaluate different ghost removal algorithms but also to introduce a methodology to evaluate such algorithms and to present some of the challenges that exist in evaluating ghost removal algorithms in HDR images. Optical flow algorithms have been shown to produce successful results in aligning input images before merging them into an HDR image. As a result one of the state-of-the-art deghosting algorithm for HDR image alignment is based on optical flow. To test the limits of the evaluated deghosting algorithms the scenes used in our experiments were selected following the criteria proposed by Baker et al. [2011], which is considered as de facto standard for evaluating optical flow methodologies. The scenes used in the experiments serve to provide challenges that need to be dealt with by not only algorithms based on optical flow methodologies but also by other ghost removal algorithms for HDR imaging. The results reveal the scenes for which the evaluated algorithms fail and may serve as a guide for future research in this area.