Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization

Liu, Min; Zhang, Xinggan; Tang, Lan

doi:10.1109/access.2019.2960078

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…According to the influence of color on people's visual perception, color is divided into cold tone, warm tone, and neutral tone [13]. Every spring, everything recovers, the weather is warm and cold, the temperature rises, giving people a kind of hope, at this time the color should be light green as the main tone.…”

Section: Color Deviation Analysismentioning

confidence: 99%

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Hu¹,

Hou

Song³

2022

Wireless Communications and Mobile Computing

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In order to improve the matching effect of environmental art color automatic matching, a model of environmental art color automatic matching based on visual matching is designed. Firstly, it preprocesses the color matching of environmental art, then extracts the color space and its main color, and analyzes the color deviation. At the same time, the color model is established and the color quantization is processed, and finally the color matching is realized. The experimental results show that the matching time of the proposed model is less than that of the traditional model, and the wrong contour is less. Research Objective. A model of environmental art color-automated matching based on visual matching is developed in order to improve the matching effect of environmental art color matching. Current Challenges. Because of time constraints, the matching model still has certain drawbacks, necessitating more optimization research in the subsequent study. The main steps in the research are pretreatment before color matching of environmental art, color space and main color extraction, color deviation analysis, color modeling, color quantization processing, implementation of color matching, and experimental comparison.

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Section: Color Deviation Analysismentioning

confidence: 99%

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Hu¹,

Hou

Song³

2022

Wireless Communications and Mobile Computing

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“…In this section, we first evaluate the denoising performance of the proposed method on one synthetic and two public realistic image datasets, and then we compare it with nine representative methods, including color block-matching 3D filtering (CBM3D) [7], multi-channel weighted nuclear norm minimization (MCWNNM) [38], guided image denoisng (GID) [36], trilateral weighted sparse coding (TWSC) [37], multi-channel weighted Schatten-p norm minimization (MCWSNM) combining WSNM in [35] and MCWNNM in [38] for color images denoising, color multi-spectral t-SVD (CMSt-SVD) [18], denoising convolutional neural networks (DnCNN) [42], fast and flexible denoising network (FFDNet) [43] and weighted tensor nuclear norm minimization (WTNNM) [20]. All the denoising results of compared methods are obtained via the source codes released from the authors' website by fine-tuned parameters.…”

Section: Resultsmentioning

confidence: 99%

“…The tensor nuclear norm is defined as the sum of the matrix nuclear norm of each tensor frontal slices in Fourier space. As matrix nuclear norm minimization tends to over-shrink the rank components and treats each rank component equally, the tensor nuclear norm minimization has similar restrictions and the weighted tensor nuclear norm minimization is introduced in [20].…”

Section: Introductionmentioning

confidence: 99%

Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising

2020

Self Cite

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In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using nonconvex t-Schatten-p (0 < p < 1) norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank. To avoid overshrink the tensor tubal rank components, a flexible weighted t-Schatten-p norm model is proposed with weights assigned to different elements of tensor singular tubes. We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently. Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively. INDEX TERMS Weighted tensor Schatten-p norm, low-rank, non-local patch group tensor.

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“…Multi‐channel weighted nuclear norm minimization for real colour image denoising (MCWNNM) method is usually easy to lose detailed information in practice, and may also produce some colour artifacts. In the real colour image denoising, there is also the problem of recovering low‐rank tensor based on t‐product weighted tensor NNM (WTNNM) [21]: It uses self‐similarity and cross‐channel correlations simultaneously, connect each patch in the RGB channel into a vector and use WNNM respectively, which mainly involves the decomposition and representation of the tensor rank. Similar problems occur with WTNNM and MCWNNM in applications.…”

Section: Introductionmentioning

confidence: 99%

Infrared object detection via patch‐tensor model and image denoising based on weighted truncated Schatten‐p norm minimization

Zhu

Gong

Liu

et al. 2023

IET Image Processing

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The nuclear norm minimization (NNM) is a special non‐convex rank minimization convex relaxation scheme for image denoising and object detection that requires denoising and background subtraction. Considering excessive shrinkage of rank components and equal treatment of different rank components, NNM is extended to the weighted Schatten‐p norm minimization (WSNM) with weights assigned to different singular values. In this paper, a multi‐channel weighted truncated WSNM model based on the WSNM optimization framework is proposed for RGB colour image denoising. On the basis of different noise intensities and non‐local self‐similar patches of each channel of the colour image itself, the proposed model is improved significantly by the optimization methods of superposition and truncation. Meanwhile, it can be generalized to the tensor space and employed to the infrared imaging target detection based on the spatial‐temporal tensor model for the first time. And the efficient alternating direction multiplier‐based algorithms are developed to solve the proposed model and the accuracy of the algorithm is effectively improved by choosing an adaptive threshold. Extensive experiments on real infrared data verified the proposed method state‐of‐the‐arts and effectiveness.

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Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization

Cited by 6 publications

References 38 publications

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising

Infrared object detection via patch‐tensor model and image denoising based on weighted truncated Schatten‐p norm minimization

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