Learning high-order MRF priors of color images
ICML '06 Proceedings of the 23rd international conference on Machine learning
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Multiscale Photon-Limited Spectral Image Reconstruction
SIAM Journal on Imaging Sciences
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
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X-ray images of distant stars and galaxies are typically registered by low photon counts at the pixel level, for which the Poisson distribution is a sensible model description. The resulting count data can be represented in a multi-scale framework, where the likelihood function factorizes in functions of relative intensity parameters corresponding to different levels from the whole frame down to the pixel level. In a Bayesian approach, a prior is assigned on these relative intensity parameters independently across levels and the image is reconstructed using the posterior mean of intensity parameter of each pixel. A novel prior which allows ties in the values of relative intensity parameters of neighboring regions has been recently shown to be very successful in finding structures in images. We extend this idea to reconstruct colored images from noisy data. The proposed method is completely data-driven, since all smoothing parameters are automatically estimated from the data without any additional user input. In the context of astronomical X-ray images, color represents the energy level of photons, which are also typically recorded by telescopes. The energy level can be considered as the third dimension of images. In a more general sense, the technique we develop applies to all three dimensional images, and can be used to process medical images as well.