Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
نویسندگان
چکیده
منابع مشابه
Weighted Schatten $p$-Norm Minimization for Image Denoising with Local and Nonlocal Regularization
This paper presents a patch-wise low-rank based image denoising method with constrained variational model involving local and nonlocal regularization. On one hand, recent patch-wise methods can be represented as a low-rank matrix approximation problem whose convex relaxation usually depends on nuclear norm minimization (NNM). Here, we extend the NNM to the nonconvex schatten p-norm minimization...
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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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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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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3016777