Depth maps captured by multiple sensors often suffer from poor resolution and missing pixels caused by low reflectivity and occlusions in the scene. To address these problems, we propose a combined framework of patch-based inpainting and super-resolution. Unlike previous works, which relied solely on depth information, we explicitly take advantage of the internal statistics of a depth map and a registered highresolution texture image that capture the same scene. We account these statistics to locate non-local patches for hole filling and constrain the sparse coding-based super-resolution problem. Extensive evaluations are performed and show the state-of-the-art performance when using real-world datasets.
S. Ikehata, J. Cho, K. Aizawa: "Depth Map Inpainting and Super-Resolution based on Internal Statistics of Geometry and Appearance"; Poster: IEEE International Conference on Image Processing, Melbourne, Australia; 09-15-2013 - 09-18-2013; in: "Proc. of ICIP", IEEE, (2013), 938 - 942.
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