Ghost-free high dynamic range imaging via rank minimization

Chul Lee, Yuelong Li, Vishal Monga

    Research output: Contribution to journalArticlepeer-review

    56 Scopus citations

    Abstract

    We propose a ghost-free high dynamic range (HDR) image synthesis algorithm using a low-rank matrix completion framework, which we call RM-HDR. Based on the assumption that irradiance maps are linearly related to low dynamic range (LDR) image exposures, we formulate ghost region detection as a rank minimization problem. We incorporate constraints on moving objects, i.e., sparsity, connectivity, and priors on under-and over-exposed regions into the framework. Experiments on real image collections show that the RM-HDR can often provide significant gains in synthesized HDR image quality over state-of-the-art approaches. Additionally, a complexity analysis is performed which reveals computational merits of RM-HDR over recent advances in deghosting for HDR.

    Original languageEnglish (US)
    Article number6814772
    Pages (from-to)1045-1049
    Number of pages5
    JournalIEEE Signal Processing Letters
    Volume21
    Issue number9
    DOIs
    StatePublished - Sep 2014

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics

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