Ghost-free high dynamic range imaging via rank minimization

Chul Lee, Yuelong Li, Vishal Monga

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


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
Issue number9
StatePublished - Sep 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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