Resolution-independent Up-sampling for Depth Map Using Fractal Transforms
Meiqin Liu, Yao Zhao, Chunyu Lin, Huihui Bai, Chao Yao · KSII Transactions on Internet and Information Systems · 2016
Due to the limitation of the bandwidth resource and capture resolution of depth cameras, low resolution depth maps should be up-sampled to high resolution so that they can correspond to their texture images.In this paper, a novel depth map up-sampling algorithm is proposed by exploiting the fractal internal self-referential feature.Fractal parameters which are extracted from a depth map, describe the internal self-referential feature of the depth map, do not introduce inherent scale and just retain the relational information of the depth map, i.e., fractal transforms provide a resolution-independent description for depth maps and could up-sample depth maps to an arbitrary high resolution.Then, an enhancement method is also proposed to further improve the performance of the up-sampled depth map.The experimental results demonstrate that better quality of synthesized views is achieved both on objective and subjective performance.Most important of all, arbitrary resolution depth maps can be obtained with the aid of the proposed scheme.