9 January 1998 Iterative regularized mixed-norm image restoration algorithm
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Proceedings Volume 3309, Visual Communications and Image Processing '98; (1998); doi: 10.1117/12.298374
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States
Abstract
This paper introduces a regularized mixed-norm image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed.A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals, and a function of the previous two functionals an the smoothing functional is utilized for determining the regularization parameter. The two parameters are chosen in such a way that the proposed functional is convex, so that a local minimizer becomes a global minimizer. The novelty of the proposed algorithm is than no knowledge of the noise distribution is required, and the relative contribution of the LMS, the LMF and the smoothing functional is adjusted based on the partially restored image.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min-Cheol Hong, Tania Stathaki, Aggelos K. Katsaggelos, "Iterative regularized mixed-norm image restoration algorithm", Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); doi: 10.1117/12.298374; https://doi.org/10.1117/12.298374
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KEYWORDS
Image restoration

Signal to noise ratio

Smoothing

Image processing

Interference (communication)

Chemical elements

Applied sciences

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