Depth-map inpainting using learned patch-based propagation

Nikolay Gapon; Viacheslav Voronin; Roman Sizyakin; Marina Zhdanova; Alexander Zelensky

doi:10.1117/12.2559452

31 January 2020 Depth-map inpainting using learned patch-based propagation

Nikolay Gapon, Viacheslav Voronin, Roman Sizyakin, Marina Zhdanova, Alexander Zelensky

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143338 (2020) https://doi.org/10.1117/12.2559452
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

This paper proposed a patch-based inpainting algorithm for depth map reconstruction using a stereo pair image. The proposed approach is based on a geometric model for patch synthesis. The lost pixels recovered by copying pixel values from the source based on a similarity criterion. We used a trained neural network to choose “best similar” patch. Experimental results show that the proposed method provides better results than the state-of-the-art methods in both subjective and objective measurements for depth map reconstruction.

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Nikolay Gapon, Viacheslav Voronin, Roman Sizyakin, Marina Zhdanova, and Alexander Zelensky "Depth-map inpainting using learned patch-based propagation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143338 (31 January 2020); https://doi.org/10.1117/12.2559452

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KEYWORDS

Cameras

Neural networks

Image processing

Image restoration

Reconstruction algorithms

Robotics

Intelligence systems

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