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Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization.
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-08-07 , DOI: 10.1155/2020/8392032
Yanwei Zhao 1 , Ping Yang 1 , Qiu Guan 1 , Jianwei Zheng 1 , Wanliang Wang 1
Affiliation  

In image denoising (IDN) processing, the low-rank property is usually considered as an important image prior. As a convex relaxation approximation of low rank, nuclear norm-based algorithms and their variants have attracted a significant attention. These algorithms can be collectively called image domain-based methods whose common drawback is the requirement of great number of iterations for some acceptable solution. Meanwhile, the sparsity of images in a certain transform domain has also been exploited in image denoising problems. Sparsity transform learning algorithms can achieve extremely fast computations as well as desirable performance. By taking both advantages of image domain and transform domain in a general framework, we propose a sparsifying transform learning and weighted singular values minimization method (STLWSM) for IDN problems. The proposed method can make full use of the preponderance of both domains. For solving the nonconvex cost function, we also present an efficient alternative solution for acceleration. Experimental results show that the proposed STLWSM achieves improvement both visually and quantitatively with a large margin over state-of-the-art approaches based on an alternatively single domain. It also needs much less iteration than all the image domain algorithms.

中文翻译:

使用稀疏变换学习和加权奇异值最小化进行图像降噪。

在图像降噪(IDN)处理中,通常将低等级属性视为重要的图像。作为低秩的凸松弛近似,基于核规范的算法及其变体引起了极大的关注。这些算法可以统称为基于图像域的方法,其共同缺点是,对于某些可接受的解决方案,需要进行大量的迭代。同时,在图像去噪问题中还利用了在特定变换域中的图像稀疏性。稀疏变换学习算法可以实现极快的计算以及理想的性能。通过在通用框架中同时利用图像域和变换域的优势,我们针对IDN问题提出了一种稀疏的变换学习和加权奇异值最小化方法(STLWSM)。所提出的方法可以充分利用两个领域的优势。为了解决非凸成本函数,我们还提出了一种有效的加速替代解决方案。实验结果表明,所提出的STLWSM在视觉和定量上均实现了改进,与基于替代性单个域的最新方法相比,具有很大的优势。与所有图像域算法相比,它还需要更少的迭代。
更新日期:2020-08-08
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