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Exact(6)
We then performed mean fusion approach to combine the images which is formally a multiple-image SR method using L 2-norm minimization [2].
To solve this problem, the state-of-the-art Retinex methods incorporate with the variational optimization method using l 1- and l 2-norm minimization [15, 16].
Ravi et al. proposed variational optimization-based method using l 1-norm minimizathat that can suppress noise while preserving the sharp edge [23].
This is essentially taking the pixel-wise mean of the images that has been proven by Farsiu et al. (2004) as a multiple-image SR method using L 2-norm minimization [2].
This is essentially taking the pixel-wise mean that has been proven by Farsiu et al. (2004) as a multiple-image SR method using L 2-norm minimization [2].
Alternative methods have been developed for highly dimensional datasets (Meinshausen and Buhlmann 2006; Friedman et al. 2008; Yuan 2010; Cai et al. 2011; Ravikumar et al. 2011), most of which have used Lasso, a shrinkage and selection method using L 1 regularization, a popular approach to deal with highly dimensional datasets (Tibshirani 1996; Hastie et al. 2009).
Similar(54)
We evaluated similar-case-based beam arrangements suggested by the proposed method using (7) based on the Euclidean distance of the 11 planning evaluation indices.
Existing Retinex-based variational optimization methods using l 2-norm minimization can reduce the noise with a large regularization parameter at the cost of blurred edge [19].
Recently, more efficient methods were developed such as an improved splint ligation method using T4 DNA ligase (6) and a photo-cross-linked ligation method using psoralen (7).
For practicing engineers, a method using L-moments for estimating the common trace length distributions is also suggested.
Specially, a fast SMKL method using L∞-norm constraint is focused, which needs no MKL optimization process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com