Evaluating deghosting algorithms for HDR images

The real world encompasses a high range of luminances. In order to capture and represent this range correctly, High Dynamic Range (HDR) imaging techniques are introduced. Some of these techniques are based on constructing an HDR image from several Low Dynamic Range (LDR) images with different exposures. In the capture and reconstruction phases, the HDR reproduction techniques must resolve the differences between the input LDR images due to camera and object movement. In this study, two recent approaches addressing this issue are compared using a novel dataset comprised of image sequences with varying complexity. The results are evaluated by using both objective and subjective measures

Evaluating Deghosting Algorithms for HDR Images

The real world encompasses a high range of luminances. In order to capture and represent this range correctly, High Dynamic Range (HDR) imaging techniques are introduced. Some of these techniques are based on constructing an HDR image from several Low Dynamic Range (LDR) images with different exposures. In the capture and reconstruction phases, the HDR reproduction techniques must resolve the differences between the input LDR images due to camera and object movement. In this study, two recent approaches addressing this issue are compared using a novel dataset comprised of image sequences with varying complexity. The results are evaluated by using both objective and subjective measures