当前位置: X-MOL 学术Inform. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-band weighted lp norm minimization for image denoising
Information Sciences ( IF 8.1 ) Pub Date : 2020-05-23 , DOI: 10.1016/j.ins.2020.05.049
Yanchi Su , Zhanshan Li , Haihong Yu , Zeyu Wang

Low rank matrix approximation (LRMA) has a wide range of applications in computer vision and has drawn much attention in recent years. A typical nuclear norm minimization (NNM) is often used to solve a LRMA, but this is likely to overshrink the rank components due to having the same threshold. To address this problem, we propose a flexible and precise model named multi-band weighted lp norm minimization (MBWPNM). We have reformulated it into nonconvex lp norm subproblems under certain weight conditions, and we solve these subproblems via a generalized soft-thresholding algorithm. We then adopt MBWPNM for image (grayscale, color and multispectral) denoising. The proposed MBWPNM not only guarantees a more accurate approximation with a Schatten p-norm in case of a change of noise levels, but it also considers prior knowledge, for which different rank components have varying importance. Extensive experiments on additive white Gaussian noise removal and realistic noise removal demonstrate that the proposed MBWPNM achieves better performance than several state-of-the-art algorithms.



中文翻译:

多频段加权 p 图像降噪的规范最小化

低秩矩阵逼近(LRMA)在计算机视觉中具有广泛的应用,并且近年来引起了很多关注。通常使用典型的核规范最小化(NNM)来求解LRMA,但是由于具有相同的阈值,这可能会使等级分量过度收缩。为了解决这个问题,我们提出了一种灵活而精确的模型,称为多频带加权p规范最小化(MBWPNM)。我们已经将其重新表述为非凸的p在一定权重条件下的范数子问题,我们通过广义的软阈值算法来解决这些子问题。然后,我们将MBWPNM用于图像(灰度,彩色和多光谱)去噪。所提出的MBWPNM不仅在噪声水平改变的情况下保证了利用Schatten p范数的更精确的近似,而且还考虑了先验知识,对此不同等级成分具有不同的重要性。关于加性高斯白噪声消除和实际噪声消除的大量实验表明,所提出的MBWPNM比几种最新算法具有更好的性能。

更新日期:2020-05-23
down
wechat
bug