Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization

IJCAI, pp. 637-644, 2020.

Cited by: 0|Bibtex|Views11|DOI:https://doi.org/10.24963/ijcai.2020/89
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In this paper, based on the low-rank property of the non-local self-similarity, we propose a MultiChannel Weighted Schatten p-Norm Minimization method for color image denoising

Abstract:

The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose...More

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Introduction
  • Noise corruption is inevitable during the image acquisition process and may heavily degrade the visual quality of an acquired image.
  • The first kind involves applying grayscale image denoising algorithms to each channel in a channel-wise manner.
  • These methods ignore the correlations between R, G and B channels, meaning that unsatisfactory results may be obtained.
  • The second method is to transform the RGB color image into other color spaces [Dabov et al, 2007a]
  • This transform, may change the noise distribution of the original observation data and introduce artifacts.
  • The third type of method involves making full use of the correlation information across each channel and conduct the denoising task on R, G and B channels simultaneously [Zhang et al, 2017a]
Highlights
  • Noise corruption is inevitable during the image acquisition process and may heavily degrade the visual quality of an acquired image
  • In this paper, based on the low-rank property of the nonlocal self-similar patches and the different noise strength across each channel, we propose a multi-channel weighted Schatten p-norm minimization (MCWSNM) model for RGB color image denoising
  • In this paper, based on the low-rank property of the non-local self-similarity, we propose a MultiChannel Weighted Schatten p-Norm Minimization method for color image denoising
  • For noisy color images, which generally hold different noise strength in each band, a weight matrix assigned to the noise level of each channel is introduced in order balance each channel’s the contribution to the final estimation result
  • MultiChannel Weighted Schatten p-Norm Minimization can be efficiently solved via alternating direction method of multipliers optimization framework
  • Experiments on synthetic and real datasets demonstrate that the proposed method can obtain satisfactory results on the color image denoising task
Methods
  • The image denoising task involves recovering a clean image xc from its noisy observation data yc, where c = {r, g, b}.
  • The authors assume that the noisy image is corrupted by additive white Gaussian noise nc with σc =, where σr, σg, σb denote the noise standard deviation in the R, G, B channels, respectively.
  • As a nonconvex surrogate of the rank function, the weighted Schatten p-norm of a matrix Z ∈ Rm×n is defined as Z w,Sp = (.
Conclusion
  • In this paper, based on the low-rank property of the non-local self-similarity, the authors propose a MCWSNM method for color image denoising.
  • For noisy color images, which generally hold different noise strength in each band, a weight matrix assigned to the noise level of each channel is introduced in order balance each channel’s the contribution to the final estimation result.
  • Experiments on synthetic and real datasets demonstrate that the proposed method can obtain satisfactory results on the color image denoising task
Summary
  • Introduction:

    Noise corruption is inevitable during the image acquisition process and may heavily degrade the visual quality of an acquired image.
  • The first kind involves applying grayscale image denoising algorithms to each channel in a channel-wise manner.
  • These methods ignore the correlations between R, G and B channels, meaning that unsatisfactory results may be obtained.
  • The second method is to transform the RGB color image into other color spaces [Dabov et al, 2007a]
  • This transform, may change the noise distribution of the original observation data and introduce artifacts.
  • The third type of method involves making full use of the correlation information across each channel and conduct the denoising task on R, G and B channels simultaneously [Zhang et al, 2017a]
  • Methods:

    The image denoising task involves recovering a clean image xc from its noisy observation data yc, where c = {r, g, b}.
  • The authors assume that the noisy image is corrupted by additive white Gaussian noise nc with σc =, where σr, σg, σb denote the noise standard deviation in the R, G, B channels, respectively.
  • As a nonconvex surrogate of the rank function, the weighted Schatten p-norm of a matrix Z ∈ Rm×n is defined as Z w,Sp = (.
  • Conclusion:

    In this paper, based on the low-rank property of the non-local self-similarity, the authors propose a MCWSNM method for color image denoising.
  • For noisy color images, which generally hold different noise strength in each band, a weight matrix assigned to the noise level of each channel is introduced in order balance each channel’s the contribution to the final estimation result.
  • Experiments on synthetic and real datasets demonstrate that the proposed method can obtain satisfactory results on the color image denoising task
Tables
  • Table1: PSNR results (dB) of real color image CC Dataset
Download tables as Excel
Funding
  • This work was supported in part by the National Natural Science Foundation of China under Grants 61822113, 61976161, 62041105, the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant 2019AEA170, the Natural Science Foundation of Hubei Province under Grants 2018CFA050, the Fundamental Research Funds for the Central Universities under Grant 413000092 and 413000082
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