Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation. Mateos, J., Lopez, A., Vega, M., Molina, R., & Katsaggelos, A. K. In 2016 IEEE International Conference on Image Processing (ICIP), volume 2016-Augus, pages 2678–2682, sep, 2016. IEEE. ![Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Passive Millimeter Wave Images currently used to detect hidden threats suffer from low resolution, blur, and a very low signal-to-noise-ratio. These shortcomings render threat detection, both visual and automatic, very challenging. Furthermore, due to the presence of very severe noise, most of the blind image restoration methods fail to recover the system blurring kernel from a single image. In this paper we propose a robust Bayesian multiframe blind image deconvolution method that approximates the posterior distribution of the blur by a Dirichlet distribution. We show that this approach naturally incorporates the non-negativity and normalization constraints for the blur and cope well with the image noise. The performance of the proposed method is tested on both synthetic and real images.
@inproceedings{Javier2016,
abstract = {Passive Millimeter Wave Images currently used to detect hidden threats suffer from low resolution, blur, and a very low signal-to-noise-ratio. These shortcomings render threat detection, both visual and automatic, very challenging. Furthermore, due to the presence of very severe noise, most of the blind image restoration methods fail to recover the system blurring kernel from a single image. In this paper we propose a robust Bayesian multiframe blind image deconvolution method that approximates the posterior distribution of the blur by a Dirichlet distribution. We show that this approach naturally incorporates the non-negativity and normalization constraints for the blur and cope well with the image noise. The performance of the proposed method is tested on both synthetic and real images.},
author = {Mateos, Javier and Lopez, Antonio and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2016.7532845},
isbn = {978-1-4673-9961-6},
issn = {15224880},
keywords = {Blind image deconvolution,Passive millimeter wave imaging,Variational Dirichlet},
month = {sep},
pages = {2678--2682},
publisher = {IEEE},
title = {{Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation}},
url = {http://ieeexplore.ieee.org/document/7532845/},
volume = {2016-Augus},
year = {2016}
}
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