An Improved Weighted Nuclear Norm Minimization Method for Image Denoising
نویسندگان
چکیده
منابع مشابه
Supplementary Materials to “Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising”
The inequality in the second last step can be proved as follows: given the diagonal matrix Σk, we define Σ k as the i-th element of Σk. If Σ k ≥ wi ρk , we have Swi ρk (Σ k ) = Σ ii k − wi ρk . If Σ k < wi ρk , we have Swi ρk (Σ k ) = 0. Overall, we have |Σ k − Swi ρk (Σ ii k )| ≤ wi ρk and hence the inequality holds. Hence, the sequence {Ak} is upper bounded. 2. Secondly, we prove that the seq...
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Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank com...
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Nuclear norm minimization (NNM) tends to over-shrink the rank components and treats the different rank components equally, thus limits its capability and flexibility. Recent studies have shown that the weighted nuclear norm minimization (WNNM) is expected to be more accurate than NNM. However, it still lacks a plausible mathematical explanation why WNNM is more accurate than NNM. This paper ana...
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In recent years, the nuclear norm minimization (NNM) problem has been attracting much attention in computer vision and machine learning. The NNM problem is capitalized on its convexity and it can be solved efficiently. The standard nuclear norm regularizes all singular values equally, which is however not flexible enough to fit real scenarios. Weighted nuclear norm minimization (WNNM) is a natu...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2929541