Zhejiang University of Technology, HangZhou 310023, China.
Comput Intell Neurosci. 2020 Aug 4;2020:8392032. doi: 10.1155/2020/8392032. eCollection 2020.
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.
在图像去噪（IDN）处理中，低秩特性通常被视为一种重要的图像先验。核范数是低秩的凸松弛逼近，基于核范数的算法及其变体引起了人们的广泛关注。这些算法通常被称为基于图像域的方法，其共同的缺点是需要大量的迭代才能得到一些可接受的解。同时，图像在特定变换域中的稀疏性也被应用于图像去噪问题。稀疏变换学习算法可以实现极快的计算速度和理想的性能。为了在一个通用框架中充分利用图像域和变换域的优势，我们提出了一种用于 IDN 问题的稀疏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域的优势。为了解决非凸代价函数，我们还提出了一种有效的加速替代方案。实验结果表明，与基于单一域的最新方法相比，所提出的 STLWSM 在视觉和数量上都有显著的改进，而且所需的迭代次数比所有基于图像域的算法都要少得多。
