Studies On Image Completion Based On The Large Displacement View

Image completion concerns the problem of removing the unwanted objects or filling in the missing regions on an image with the available information from the same image or another to generate visually plausible result. Due to wide applications in photo editing, special effects production and digital culture heritage, image completion has been a hot topic in computer graphics, computer vision and image processing.This thesis focuses on image completion based on the views of large displacement, which introduce one large displacement view (LDV) image to improve the illness nature of traditional image completion methods. The key challenges here are how to convert the visible information on the LDV image to be useful and how to exploit them to repair the target image.First, we propose an interactive segmentation of planar scenes based approach. With the help of user interaction, our algorithm first decomposes the target image and the LDV one into several corresponding planar scene regions (PSRs) and transforms the candidate PSRs on LDV image onto the target image. Then we develop a new image repairing algorithm, coupled with graph cut based image stitching, texture synthesis based boundary inpainting, and image fusion based hole filling, to complete the damaged regions seamlessly. Finally, the ghost effect between the repaired region and its surroundings is eliminated by Poisson image blending.Then, we note that the PSRs on the LDV image don't agree to the planar assumption entirely, perspective distortions present in the warped PSRs to certain degree. A coarse-to-fine distortion correction algorithm is proposed to eliminate the perspective distortions, and an approach based on the minimization of warped perspective distortions for the LDV image is put forward to restore the target region. First, under the assumption of a planar scene, the LDV image is warped according to a homography matrix to generate the initial correction result. Second. the remaining perspective distortion in the common scene regions is relaxed by energy optimization of overlapping correspondences, with the expectations of color constancy and displacement field smoothness. Third, under the constraints of epipolar geometry, displacement field smoothness and color consistency of the neighboring pixels, the missing pixels are orderly repaired according to a specially defined priority function.Finally, we present an algorithm based on multi-level scene clustering and view-consistent composition. Evolving the traditional single-model fitting method to multi-model fitting, a coarse-to-fine multi-level scene clustering scheme is proposed to simultaneously cluster the feature correspondences and reject outliners between the LDV image and the target image. As a result, it segments the multi-body dynamic scene into several dynamic objects in terms of the epipolar geometry model (expressed by the fundamental matrix), and segments the static scene into several approximate planar scene regions (APSRs) in terms of the planar scene model (represented with the homography). Then, employing montage and structural displacement propagation (SDP), a view-consistent image composition algorithm stitches and completes the missing area with three steps, i.e. SDP based distortion preprocessing, montage based hole stitching and SDP based distortion postprocessing.Experimental results demonstrate that our methods outperform recent state-of-art image completion algorithms, especially for repairing large missing area with complex structure information.