Illumination Estimation via Non-Negative Matrix Factorization
Shi, L., Funt, B., Xiong, W., Kim, S., Kang, B., and Lee, S.D.,
"Illumination Estimation via Non-negative Matrix Factorization,"
Proc. AIC 2007 Color for Science and Industry, Midterm Meeting of the International Color Association , Hangzhou, July 2007.
Abstract:
The problem of illumination estimation for colour constancy and automatic white
balancing of digital
color imagery can be viewed as the separation of the image into illumination and reflectance
components.
We propose using nonnegative matrix factorization with sparseness constraints (NMFsc) to
separate the
components. Since illumination and reflectance are combined multiplicatively, the first step is
to move to
the logarithm domain so that the components are additive. The image data is then organized as a
matrix to
be factored into nonnegative components. Sparseness constraints imposed on the
resulting factors help
distinguish illumination from reflectance. Experiments on a large set of real
images demonstrate
accuracy that is competitive with other illumination-estimation algorithms. One advantage of the
NMFsc
approach is that, unlike statistics- or learning-based approaches, it requires no calibration or
training.
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