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

Published: 01 November 2012 Publication History

Abstract

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (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, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.

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    Published In

    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 31, Issue 6
    November 2012
    794 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/2366145
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 01 November 2012
    Published in TOG Volume 31, Issue 6

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    Author Tags

    1. anti-aliasing
    2. bilateral filtering
    3. display-aware filtering
    4. image pyramids
    5. local Laplacian filtering
    6. mipmapping
    7. multi-resolution filtering
    8. smoothed local histogram filtering

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