Sparse PDF maps for non-linear multi-resolution image operations

Abstract

We introduce a new type of multi-resolution image pyramid for high-resolution images called <i>sparse pdf maps</i> (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, <i>O</i>(<i>n</i>) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.

DOI: 10.1145/2366145.2366152

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Cite this paper

@article{Hadwiger2012SparsePM, title={Sparse PDF maps for non-linear multi-resolution image operations}, author={Markus Hadwiger and Ronell Sicat and Johanna Beyer and Jens H. Kr{\"{u}ger and Torsten M{\"{o}ller}, journal={ACM Trans. Graph.}, year={2012}, volume={31}, pages={133:1-133:12} }