Automatic noise modeling for ghost-free HDR reconstruction

Miguel Granados, Kwang In Kim, James Tompkin, Christian Theobalt

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

High dynamic range reconstruction of dynamic scenes requires careful handling of dynamic objects to prevent ghosting. However, in a recent review, Srikantha et al. [2012] conclude that "there is no single best method and the selection of an approach depends on the user's goal". We attempt to solve this problem with a novel approach that models the noise distribution of color values. We estimate the likelihood that a pair of colors in different images are observations of the same irradiance, and we use a Markov random field prior to reconstruct irradiance from pixels that are likely to correspond to the same static scene object. Dynamic content is handled by selecting a single low dynamic range source image and hand-held capture is supported through homography-based image alignment. Our noise-based reconstruction method achieves better ghost detection and removal than state-of-the-art methods for cluttered scenes with large object displacements. As such, our method is broadly applicable and helps move the field towards a single method for dynamic scene HDR reconstruction.
Original languageEnglish
Pages (from-to)201:1-201:10
Number of pages10
JournalACM Transactions on Graphics
Volume32
Issue number6
DOIs
Publication statusPublished - Nov 2013

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    Granados, M., Kim, K. I., Tompkin, J., & Theobalt, C. (2013). Automatic noise modeling for ghost-free HDR reconstruction. ACM Transactions on Graphics, 32(6), 201:1-201:10. https://doi.org/10.1145/2508363.2508410