| 引用本文: | 郭昕刚,许连杰,程超,等.加权核范数最小化和改进小波阈值函数的图像去噪算法.[J].国防科技大学学报,2024,46(2):238-246.[点击复制] |
| GUO Xingang,XU Lianjie,CHENG Chao,et al.Image denoising algorithm based on weighted kernel norm minimization and improved wavelet threshold function[J].Journal of National University of Defense Technology,2024,46(2):238-246[点击复制] |
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| 加权核范数最小化和改进小波阈值函数的图像去噪算法 |
| 郭昕刚,许连杰,程超,霍金花 |
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(长春工业大学 计算机科学与工程学院, 吉林 长春 130012)
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| 摘要: |
| 针对加权核范数最小化算法存在结构残余噪声以及无法较好地保持图像边缘结构的问题,提出基于加权核范数最小化和改进小波阈值函数的图像去噪算法。利用全变分模型对噪声图像进行初步去噪,使用噪声图像与初步去噪后的图像进行差分运算,对差分后得到的噪声残差图像使用改进的小波阈值函数去噪,将小波去噪后的残差图像与初步去噪图像叠加,将叠加后的图像使用基于残余噪声水平迭代的加权核范数最小化算法进行二次去噪。相较于当下主流去噪算法,经该算法处理后的图像的PSNR和SSIM值均有所提升,能够更好地保持图像的纹理结构,且在高噪声环境下效果更佳。 |
| 关键词: 加权核范数 小波变换 噪声残差 全变分 |
| DOI：10.11887/j.cn.202402024 |
| 投稿日期：2021-12-30 |
| 基金项目:吉林省教育厅基金资助项目(JKH20210754KJ) |
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| Image denoising algorithm based on weighted kernel norm minimization and improved wavelet threshold function |
| GUO Xingang, XU Lianjie, CHENG Chao, HUO Jinhua |
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(School of Science and Engineering, Changchun University of Technology, Changchun 130012, China)
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| Abstract: |
| In view of the structural residual noise in the weighted nuclear norm minimization algorithm and the inability to maintain the edge structure of the image, a denoising method that minimizes the weighted kernel norm and improves the wavelet threshold was adopted. The total variation model to perform preliminary denoising of the noise image, and the noisy image to subtract the preliminary denoised image were used. An improved wavelet threshold function was used to denoise the noise difference image obtained after subtraction. The denoised residual image was superimposed with the preliminary denoised image, and the superimposed image was finally denoised using an iterative weighted kernel norm minimization algorithm based on the residual noise level. Compared with the more popular denoising algorithms, the PSNR and SSIM processed by this algorithm are improved, the texture structure of the image can be maintained, and the effect is better in a high-noise environment. |
| Keywords: weighted kernel norm wavelet transform noise residual total variation |
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