High dynamic range image deghosting by fast approximate background modelling

High Dynamic Range (HDR) images of real world scenes often suffer from ghosting artifacts caused by motion in the scene. Existing solutions to this problem typically either only address specific types of ghosting, or are very computationally expensive.We address ghosting by performing change detection on exposure-normalized images, then reducing the contribution of moving objects to the final composite on a frame-by-frame basis. Change detection is computationally advantageous and it can be applied to images exhibiting varied ghosting artifacts. We demonstrate our method both for Low Dynamic Range (LDR) and HDR images. Additional constraints based on a priori knowledge of the changing exposures apply to HDR images. We increase the stability of our approach by using recent superpixel segmentation techniques to enhance the change detection. Our solution includes a novel approach for areas that see motion throughout the capture, e.g., foliage blowing in the wind.We demonstrate the success of our approach on challenging ghosting scenarios, and that our results are comparable to existing state-of- the-art methods, while providing computational savings over these methods.

► Fast HDR image deghosting using change detection.
► Refinement of ghost detection using state of the art superpixel oversegmentation.
► Novel classification of ghosts as “discrete” (people, cars) or “fluid” (flags, waves).
► A novel approach for detecting and freezing “fluid” ghosts.
► Method provides a fast approximate background model, with ghosts removed or frozen.