Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation

LI Xiao-yao; WANG Lian-hong; ZHOU Yi-cong; ZHANG Jing

doi:10.12263/DZXB.20210790

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Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation

PAPERS | 更新时间：2025-12-08

- Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation
- ACTA ELECTRONICA SINICA Vol. 51, Issue 4, Pages: 975-983(2023)
- 作者机构：
  
  1.湖南大学电气与信息工程学院，湖南长沙 410082
  2.澳门大学电脑与资讯科学系，澳门 999078
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210790
  CLC： TN911.73;
- Received：24 June 2021，
  
  Revised：2021-12-29，
  
  Published：25 April 2023
- 稿件说明：
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李潇瑶,王炼红,周怡聪等.基于四元数非局部低秩和全变分的图像混合噪声去噪算法[J].电子学报,2023,51(04):975-983.

LI Xiao-yao,WANG Lian-hong,ZHOU Yi-cong,et al.Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation[J].ACTA ELECTRONICA SINICA,2023,51(04):975-983.
李潇瑶,王炼红,周怡聪等.基于四元数非局部低秩和全变分的图像混合噪声去噪算法[J].电子学报,2023,51(04):975-983. DOI： 10.12263/DZXB.20210790.

LI Xiao-yao,WANG Lian-hong,ZHOU Yi-cong,et al.Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation[J].ACTA ELECTRONICA SINICA,2023,51(04):975-983. DOI： 10.12263/DZXB.20210790.

摘要

许多彩色图像去噪算法没有充分利用图像块间和颜色分量间的相关性，在去噪时丢失大量细节，容易导致颜色失真，从而影响后续处理.此外，真实的图像噪声通常是高斯-脉冲混合噪声而不是单一类型的，导致许多成熟的仅针对加性高斯噪声或脉冲噪声的去噪算法无法直接使用于真实场景.为解决这些问题，本文提出了基于四元数非局部低秩和全变分的图像混合噪声去除算法.该算法首先将彩色图像从空间域转换至四元数域，然后计算图像的非局部结构相似性和局部梯度，利用四元数域下的L

范数最小化模型，最终实现图像去噪.与现有的彩色图像去噪算法相比，该算法能更有效地保留图像块间、块内以及颜色分量间的相关性.去噪实验结果表明，本文算法在峰值信噪比和结构相似性上分别提高0.21~3.04 dB和1.51%~14.51%，并能在有效去噪和抑制伪影的同时，更好地保持图像细节和颜色信息，对噪声类型和强度变化更具鲁棒性.

Abstract

Many color image denoising methods fail to fully consider the correlations among color channels and lose many details. These always cause color distortion in the denoising results and even affect subsequent image processing tasks. In addition

the realistic noise often consists of different types of noise

such as the mixed Gaussian-impulsive noise

instead of single type. This leads to the failure of direct application in real-world image denoising for some existing denoising methods aiming at only additional Gaussian noise or only impulsive noise. To solve these problems

this paper proposes a mixed noise removal method named quaternion-based non-local low rank and total variation. The proposed method first converts the color image from spatial domain to quaternion domain

captures the non-local similarity and local gradient information and then applies the quaternion-based L

-regularized minimization model to denoise color images. Compared with many e

xisting color denoising methods

the proposed method can keep the within-patch

cross-patch and cross-channel correlations of color images. Compared with other competing methods

the proposed method improves the peak signal-to-noise ratio and structure similarity by 0.21~3.04 dB and 1.51%~14.51%

respectively. The visual results demonstrate the superiority of the proposed method in preserving image details and color information while removing noise and reducing artifacts. Furthermore

the proposed method is robust to noise type and noise level.

关键词

Keywords

references

吴绿 , 张馨月 , 唐茉 , 等 . Focus+Context语义表征的场景图像分割 [J ] . 电子学报 , 2021 , 49 ( 3 ): 596 - 604 .

WU L , ZHANG X Y , TANG M , et al . Focus+Context semantic representation in scene segmentation [J ] . Acta Electronica Sinica , 2021 , 49 ( 3 ): 596 - 604 . (in Chinese)

徐少平 , 林珍玉 , 张贵珍 , 等 . 采用深度学习与图像融合混合实现策略的低照度图像增强算法 [J ] . 电子学报 , 2021 , 49 ( 1 ): 72 - 76 .

XU S P , LIN Z Y , ZHANG G Z , et al . A low-light image enhancement algorithm using the hybrid strategy of deep learning and image fusion [J ] . Acta Electronica Sinica , 2021 , 49 ( 1 ): 72 - 76 . (in Chinese)

毛林 , 赵利强 , 于明安 , 等 . 融合先验知识特征的超声图像甲状旁腺结节识别 [J ] . 电子学报 , 2021 , 49 ( 5 ): 944 - 952 .

MAO L , ZHAO L Q , YU M G , et al . Recognition of parathyroid nodule by fusing prior knowledge features in ultrasound image [J ] . Acta Electronica Sinica , 2021 , 49 ( 5 ): 944 - 952 . (in Chinese)

黄鸿 , 徐科杰 , 石光耀 . 联合多尺度多特征的高分遥感图像场景分类 [J ] . 电子学报 , 2020 , 48 ( 9 ): 1824 - 1833 .

HUANG H , XU K J , SHI G Y . Scene classification of high-resolution remote sensing image by multi-scale and multi-feature fusion [J ] . Acta Electronica Sinica , 2020 , 48 ( 9 ): 1824 - 1833 . (in Chinese)

BUADES A , COLL B , MOREL J M . A non-local algorithm for image denoising [C ] // 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego : IEEE , 2005 : 60 - 65 .

AHARON M , ELAD M , BRUCKSTEIN A . K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation [J ] . IEEE Transactions on Signal Processing , 2006 , 54 ( 11 ): 4311 - 4322 .

GU S H , XIE Q , MENG D Y , et al . Weighted nuclear norm minimization and its applications to low level vision [J ] . International Journal of Computer Vision , 2017 , 121 ( 2 ): 183 - 208 .

CAI J F , CANDÈS E J , SHEN Z W . A singular value thresholding algorithm for matrix completion [J ] . SIAM Journal on Optimization , 2010 , 20 ( 4 ): 1956 - 1982 .

ZHANG B X , ZHU G P , ZHU Z B . A TV-log nonconvex approach for image deblurring with impulsive noise [J ] . Signal Processing , 2020 , 174 : 107631 .

HE W , ZHANG H Y , ZHANG L P , et al . Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 1 ): 178 - 188 .

PENG J J , XIE Q , ZHAO Q , et al . Enhanced 3DTV regularization and its applications on HSI denoising and compressed sensing [J ] . IEEE Transactions on Image Processing , 2020 , 29 : 7889 - 7903 .

JIANG J L , ZHANG L , YANG J . Mixed noise removal by weighted encoding with sparse nonlocal regularization [J ] . IEEE Transactions on Image Processing , 2014 , 23 ( 6 ): 2651 - 2662 .

HUANG T , DONG W S , XIE X M , et al . Mixed noise removal via Laplacian scale mixture modeling and nonlocal low-rank approximation [J ] . IEEE Transactions on Image Processing , 2017 , 26 ( 7 ): 3171 - 3186 .

XU Y , YU L C , XU H T , et al . Vector sparse representation of color image using quaternion matrix analysis [J ] . IEEE Transactions on Image Processing , 2015 , 24 ( 4 ): 1315 - 1329 .

YU Y B , ZHANG Y L , YUAN S F . Quaternion-based weighted nuclear norm minimization for color image denoising [J ] . Neurocomputing , 2019 , 332 : 283 - 297 .

YAN J , LU W S . Image denoising by generalized total variation regularization and least squares fidelity [J ] . Multidimensional Systems and Signal Processing , 2015 , 26 ( 1 ): 243 - 266 .

HU Y , ZHANG D B , YE J P , et al . Fast and accurate matrix completion via truncated nuclear norm regularization [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013 , 35 ( 9 ): 2117 - 2130 .

XIE Y , GU S H , LIU Y , et al . Weighted schatten p-norm minimization for image denoising and background subtraction [J ] . IEEE Transactions on Image Processing , 2016 , 25 ( 10 ): 4842 - 4857 .

HAMILTON W R . II. On quaternions; or on a new system of imaginaries in algebra [J ] . The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science , 1844 , 25 ( 163 ): 10 - 13 .

CHEN Y Y , XIAO X L , ZHOU Y C . Low-rank quaternion approximation for color image processing [J ] . IEEE Transactions on Image Processing , 2020 , 29 : 1426 - 1439 .

XU D P , XIA Y L , MANDIC D P . Optimization in quaternion dynamic systems: Gradient, hessian, and learning algorithms [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2016 , 27 ( 2 ): 249 - 261 .

LIU Y , ZHENG Y L , LU J Q , et al . Constrained quaternion-variable convex optimization: A quaternion-valued recurrent neural network approach [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2020 , 31 ( 3 ): 1022 - 1035 .

MARTIN D , FOWLKES C , TAL D , et al . A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics [C ] // Proceedings Eighth IEEE International Conference on Computer Vision . Vancouver : IEEE , 2001 : 416 - 423 .

KONG Z M , YANG X W . Color image and multispectral image denoising using block diagonal representation [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 9 ): 4247 - 4259 .

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