Illumination Estimation via Non-Negative Matrix Factorization
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.
Color Constancy Non-Negative Matrix Factorization Automatic White Balancing
L. Shi B. Funt W. Xiong S. S. Kim B. H. Kang S. D. Lee C. Y. Kim
Simon Fraser University, Vancouver, Canada Samsung Advanced Institute of Technology,Korea Samsung Advanced Institute of Technology, Korea
国际会议
杭州
英文
102-106
2007-07-12（万方平台首次上网日期，不代表论文的发表时间）