@inproceedings{707e3482dac84069ae44afc90ba257e9,
title = "Non-Local Noise Estimation for Adaptive Image Denoising",
abstract = "Image denoising is a classical linear inverse prob-lem with applications in remote sensing, medical imaging, astronomy and surveillance. This article addresses the image denoising problem using a non-local noise estimation based on the spatial redundancy offered by natural images. A low dimensional signal subspace is estimated using the statisti-cal strength of singular value decomposition (SVD), which reduces the computational burden and enhances the local basis screening. A multiple regression based approach is then applied on the estimated basis to calculate the observation noise and the whole image is restored by patch based processing. The proposed method is adaptive in the sense that all the algorithm parameters are learned from the observed noisy data. The simulated comparisons shows comparatively high performance of the proposed algorithm comparing to the other image denoising techniques.",
author = "Muhammad Hanif and Seghouane, \{Abd Krim\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015 ; Conference date: 23-11-2015 Through 25-11-2015",
year = "2015",
doi = "10.1109/DICTA.2015.7371290",
language = "English",
series = "2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 International Conference on Digital Image Computing",
address = "United States",
}