Paper
28 February 2012 Efficient dense blur map estimation for automatic 2D-to-3D conversion
Author Affiliations +
Proceedings Volume 8288, Stereoscopic Displays and Applications XXIII; 82882H (2012) https://doi.org/10.1117/12.907840
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
Abstract
Focus is an important depth cue for 2D-to-3D conversion of low depth-of-field images and video. However, focus can be only reliably estimated on edges. Therefore, Bea et al. [1] first proposed an optimization based approach to propagate focus to non-edge image portions, for single image focus editing. While their approach produces accurate dense blur maps, the computational complexity and memory requirements for solving the resulting sparse linear system with standard multigrid or (multilevel) preconditioning techniques, are infeasible within the stringent requirements of the consumer electronics and broadcast industry. In this paper we propose fast, efficient, low latency, line scanning based focus propagation, which mitigates the need for complex multigrid or (multilevel) preconditioning techniques. In addition we propose facial blur compensation to compensate for false shading edges that cause incorrect blur estimates in people's faces. In general shading leads to incorrect focus estimates, which may lead to unnatural 3D and visual discomfort. Since visual attention mostly tends to faces, our solution solves the most distracting errors. A subjective assessment by paired comparison on a set of challenging low-depth-of-field images shows that the proposed approach achieves equal 3D image quality as optimization based approaches, and that facial blur compensation results in a significant improvement.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
L. P. J. Vosters and G. de Haan "Efficient dense blur map estimation for automatic 2D-to-3D conversion", Proc. SPIE 8288, Stereoscopic Displays and Applications XXIII, 82882H (28 February 2012); https://doi.org/10.1117/12.907840
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KEYWORDS
Image resolution

3D image processing

Cameras

Video

Facial recognition systems

Image quality

Gaussian filters

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