Schretter, C., Sun, J., & Schelkens, P. (2018). Image Reconstruction with Smoothed Mixtures of Regressions. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 400-404). Article 8451703 IEEE. https://doi.org/10.1109/ICIP.2018.8451703

@inproceedings{99c9d1eb514f4ddc9394484f6eeb9e8d,
title = "Image Reconstruction with Smoothed Mixtures of Regressions",
abstract = "This work builds upon the kernel regression framework for solving the general image processing problem of denoising, deblurring and interpolating from scattered image samples. A competitive expectation-maximization method estimates globally all parameters of a generative image model, accounting for missing samples. One 2D footprint kernel and a local linear regression plane are estimated per data sample. Kernels can shift and their prior probabilities are estimated as well, unlike in nonparametric models. Missing data yields an underdetermined problem that is regularized by smoothing the marginal mixture density. At each iteration, a balloon estimator computes numerically the spatial {"}territory{"} associated to each data samples. Results of these numerical diffusion operations are used to convolve adaptively each kernel in the forward model. Finally, the complete image is reconstructed by smoothing regression for combining conditional means of local linear regressors. Experiments apply this iterative Bayesian technique in image restoration.",
keywords = "Density estimation, Expectation-maximization, Image reconstruction, Regularization, Sparse model",
author = "Colas Schretter and Jianyong Sun and Peter Schelkens",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451703",
language = "English",
pages = "400--404",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
publisher = "IEEE",
note = "2018 IEEE International Conference on Image Processing (ICIP) ; Conference date: 07-10-2018 Through 10-10-2018",
}