Automatic Noise Modeling for
|M. Granados||J. Tompkin||K. I. Kim||C. Theobalt|
|Bracketed exposures||Ghost-free HDR reconstruction|
High dynamic range reconstruction of dynamic scenes requires careful handling of dynamic objects to prevent ghosting. However, in a recent review, Srikantha et al.  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.
To appear in ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 2013:
The technology is patent pending and can be licensed through the IT Incubator of the Max Planck Society and Saarland University. On this website you can try out our algorithm with your own photographs.