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 language | English |
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Article number | 6814772 |
Pages (from-to) | 1045-1049 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 21 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2014 |
Keywords
- De-ghosting
- high dynamic range imaging
- low-rank matrix completion