Optimizing depth-of-field based on a range map and a wavelet transform

Mike Wellner; Thomas Käster; Thomas Martinetz; Erhardt Barth

doi:10.1117/12.2002330

7 March 2013 Optimizing depth-of-field based on a range map and a wavelet transform

Mike Wellner, Thomas Käster, Thomas Martinetz, Erhardt Barth

Author Affiliations +

Proceedings Volume 8667, Multimedia Content and Mobile Devices; 86671U (2013) https://doi.org/10.1117/12.2002330
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

The imaging properties of small cameras in mobile devices exclude restricted depth-of-field and range-dependent blur that may provide a sensation of depth. Algorithmic solutions to this problem usually fail because high- quality, dense range maps are hard to obtain, especially with a mobile device. However, methods like stereo, shape from focus stacks, and the use of ashlights may yield coarse and sparse range maps. A standard procedure is to regularize such range maps to make them dense and more accurate. In most cases, regularization leads to insufficient localization, and sharp edges in depth cannot be handled well. In a wavelet basis, an image is defined by its significant wavelet coefficients, only these need to be encoded. If we wish to perform range-dependent image processing, we only need to know the range for the significant wavelet coefficients. We therefore propose a method that determines a sparse range map only for significant wavelet coefficients, then weights the wavelet coefficients depending on the associate range information. The image reconstructed from the resulting wavelet representation exhibits space-variant, range-dependent blur. We present results based on images and range maps obtained with a consumer stereo camera and a stereo mobile phone.

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Mike Wellner, Thomas Käster, Thomas Martinetz, and Erhardt Barth "Optimizing depth-of-field based on a range map and a wavelet transform", Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86671U (7 March 2013); https://doi.org/10.1117/12.2002330

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KEYWORDS

Wavelets

Cameras

Wavelet transforms

Image segmentation

Mobile devices

Stereoscopic cameras

3D image processing

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