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.
