Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging. Amizic, B., Spinoulas, L., Molina, R., & Katsaggelos, A. K. In 2012 19th IEEE International Conference on Image Processing, pages 925–928, sep, 2012. IEEE, IEEE. ![Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We propose a novel blind image deconvolution (BID) regularization framework for compressive passive millimeter-wave (PMMW) imaging systems. The proposed framework is based on the variable-splitting optimization technique, which allows us to utilize existing compressive sensing reconstruction algorithms in compressive BID problems. In addition, a non-convex lp quasi-norm with 0 <60; p <60; 1 is employed as a regularization term for the image, while a simultaneous auto-regressive (SAR) regularization term is utilized for the blur. Furthermore, the proposed framework is very general and it can be easily adapted to other state-of-the-art BID approaches that utilize different image/blur regularization terms. Experimental results, obtained with simulations using a synthetic image and real PMMW images, show the advantage of the proposed approach compared to existing ones. © 2012 IEEE.
@inproceedings{amizic2012compressive,
abstract = {We propose a novel blind image deconvolution (BID) regularization framework for compressive passive millimeter-wave (PMMW) imaging systems. The proposed framework is based on the variable-splitting optimization technique, which allows us to utilize existing compressive sensing reconstruction algorithms in compressive BID problems. In addition, a non-convex lp quasi-norm with 0 <60; p <60; 1 is employed as a regularization term for the image, while a simultaneous auto-regressive (SAR) regularization term is utilized for the blur. Furthermore, the proposed framework is very general and it can be easily adapted to other state-of-the-art BID approaches that utilize different image/blur regularization terms. Experimental results, obtained with simulations using a synthetic image and real PMMW images, show the advantage of the proposed approach compared to existing ones. {\textcopyright} 2012 IEEE.},
author = {Amizic, Bruno and Spinoulas, Leonidas and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2012 19th IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2012.6467012},
isbn = {978-1-4673-2533-2},
issn = {15224880},
keywords = {Variable-splitting,blind image deconvolution,compressive sensing,inverse methods},
month = {sep},
organization = {IEEE},
pages = {925--928},
publisher = {IEEE},
title = {{Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging}},
url = {http://ieeexplore.ieee.org/document/6467012/},
year = {2012}
}
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