Inpainting in multi-image stereo
Proceedings of the 32nd DAGM conference on Pattern recognition
Range map superresolution-inpainting, and reconstruction from sparse data
Computer Vision and Image Understanding
Towards Unrestrained Depth Inference with Coherent Occlusion Filling
International Journal of Computer Vision
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Range images captured from range scanning devices such as laser scanners or PMD (photonic mixer device) cameras, often possess drawbacks of having low resolution and/or missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections etc. In this work, we address both the issues in a single framework. We employ Bayesian regularization for resolution enhancement and inpainting in a general multi-image super-resolution scenario. We modify the traditional image formation model used in image/range super-resolution to account for the missing regions. This modification is important to couplethe inpainting process with super-resolution. We also stress the importance of prior information in the integration andnote that we require the priors to constrain the solution dif-ferently for inpainting and for super-resolution. The proposed inhomogeneous prior handles the requirements for inpainting as well as super-resolution. The modification of the imaging model and the formulation of the inhomogeneous prior are both important for the success of the integration. Our results show inpainting of large missing regions, reduction in distortions and good preservation of details at the high-resolution.