Depth map resolution enhancement for 2D/3D imaging system via compressive sensing

Juanjuan Han; Otmar Loffeld; Klaus Hartmann

doi:10.1117/12.901006

18 August 2011 Depth map resolution enhancement for 2D/3D imaging system via compressive sensing

Juanjuan Han, Otmar Loffeld, Klaus Hartmann

Proceedings Volume 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications; 819434 (2011) https://doi.org/10.1117/12.901006
Event: International Symposium on Photoelectronic Detection and Imaging 2011, 2011, Beijing, China

Abstract

This paper introduces a novel approach for post-processing of depth map which enhances the depth map resolution in order to achieve visually pleasing 3D models from a new monocular 2D/3D imaging system consists of a Photonic mixer device (PMD) range camera and a standard color camera. The proposed method adopts the revolutionary inversion theory framework called Compressive Sensing (CS). The depth map of low resolution is considered as the result of applying blurring and down-sampling techniques to that of high-resolution. Based on the underlying assumption that the high-resolution depth map is compressible in frequency domain and recent theoretical work on CS, the high-resolution version can be estimated and furthermore reconstructed via solving non-linear optimization problem. And therefore the improved depth map reconstruction provides a useful help to build an improved 3D model of a scene. The experimental results on the real data are presented. In the meanwhile the proposed scheme opens new possibilities to apply CS to a multitude of potential applications on various multimodal data analysis and processing.

Citation Download Citation

Juanjuan Han, Otmar Loffeld, and Klaus Hartmann "Depth map resolution enhancement for 2D/3D imaging system via compressive sensing", Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 819434 (18 August 2011); https://doi.org/10.1117/12.901006

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KEYWORDS

Compressed sensing

Lawrencium

Imaging systems

3D modeling

Cameras

Super resolution

Sensors

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