Automatic and accurate image completion from a large displacement view
利用大位移视图的自动可信图像修补

In order to achieve accurate automatic image reparation, we propose a new framework for image completion based on the large displacement view(LDV)image. First, we extract the evenly distributed and quasi-dense feature-point correspondences between the target image and the LDV image by combining multiple distinct feature detectors in a complementary way. Inspired by the prior model and model fitting problem, we then devise a quasi-planar scene-regions(QPSRs)clustering algorithm, which classifies the feature point correspondences and represents a natural scene image with multiple QPSRs to remove the perspective distortions in the LDV image. Inspired by the texture synthesis and image stitching techniques, we finally present a QPSRs compositing algorithm, which corrects and stitches the re-projected QPSRs images to fill in the missing areas on the target image. Our experimental results are comparable with the ground-truth.