Super-resolution imaging with occlusion removal using a camera array
Tingtian Li, Daniel Pak-Kong Lun · 2016
In this paper, a novel algorithm which combines the super-resolution imaging and occlusion removal into a single and automatic procedure is proposed. By utilizing the visual parallax of objects at different depths and the sub-pixel information of the images captured by a camera array, we can estimate the shape of the occlusion and reconstruct the background at a higher resolution iteratively. The occlusion shape estimation is achieved by a new method called “seed growth”, which treats the detected feature points of the occlusion as “seeds”. These “seeds” will gradually grow until they reach the occlusion boundary. Experimental results show that the proposed algorithm can well remove the occlusion while super-resolving the background. It performs equally well when there are multiple occlusion objects or the object has irregular shape.