Disparity Guided Texture Inpainting for Light Field View Synthesis

Abstract

Light fields, as a type of visual content, richer in textural and geometric information than traditional imaging, can exhibit strong redundancies between views. Disparity compensated prediction, as one of the view synthesis frameworks, can exploit these redundancies to achieve high coding efficiency. Properly handling texture occlusion in the prediction process is important. We propose a disparity guided texture inpainting scheme to resolve texture occlusion. It turns out that reliable disparity (depth) can be available within occluded regions. A key contribution of this paper is the incorporation of disparity to guide the pixel visiting order and the weighted-average interpolation processes of the inpainting scheme. Specifically, the paper describes a disparity-dependent boundary distance metric, which is evaluated using a Dijkstra's algorithm and used to drive inpainting decisions. Our proposed method is evaluated on a realistic dataset with complex geometry, presenting promising results.