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

Mike Wellner, Thomas Kastera, Thomas Martinetz, Erhardt Barth


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 insuficient localization, and sharp edges in depth cannot be handled well. In a wavelet basis, an image is defined by its significant wavelet coeficients, 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 coeficients. We therefore propose a method that determines a sparse range map only for significant wavelet coeficients, then weights the wavelet coeficients 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.

Original languageEnglish
Title of host publicationMultimedia Content and Mobile Devices
Number of pages8
Publication date01.03.2013
Pages8667 - 8667 - 8
ISBN (Print)9780819494405
Publication statusPublished - 01.03.2013
EventIS&T/SPIE Electronic Imaging Conference 2013: Multimedia Content and Mobile Devices - Hyatt Regency San Francisco Airport Hotel, San Francisco, United States
Duration: 03.02.201307.02.2013


Dive into the research topics of 'Optimizing depth-of-field based on a range map and a wavelet transform'. Together they form a unique fingerprint.

Cite this