电子测量与仪器学报

鲁思琪,周先春,汪志飞.改进型自适应全变分图像降噪模型[J].电子测量与仪器学报,2022,36(6):236-243

改进型自适应全变分图像降噪模型

Improved adaptive total variational image denoising model

DOI：

英文关键词:adaptive total variational denoising differential curvature level set curvature gradient mode

基金项目:省级大学生创新训练项目（202210300147Y）资助

作者	单位
鲁思琪	1. 南京信息工程大学电子与信息工程学院
周先春	2. 南京信息工程大学人工智能学院(未来技术学院)
汪志飞	1. 南京信息工程大学电子与信息工程学院

Author	Institution
Lu Siqi	1. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology
Zhou Xianchun	2. School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology,
Wang Zhifei	1. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology

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中文摘要:

针对传统全变分去噪方法峰值信噪比不高,迭代效率较低的缺点,提出了一种新的自适应全变分去噪模型。首先,利用差分曲率改进全变分方程的正则项指数以区分出噪声点;然后,结合水平集曲率和梯度模的性质,使平滑区和边缘区达到不同的去噪效果,让新模型兼具保留边缘和平滑噪声的特点。实验结果表明,与当前 3 种主流模型相比,新模型的峰值信噪比提高了 1. 4 dB 以上,平均绝对误差也减少了 2. 5 以上,结构相似性平均提高了 0. 13,并且迭代效率至少提高了 1. 6 倍,更有利于实际应用。

英文摘要:

Aiming at the shortcomings of the traditional total variational denoising methods, such as low peak SNR and low iteration efficiency, a new adaptive total variational denoising model is proposed in this paper. Firstly, the regular exponent of the total variational equation is improved by using differential curvature to distinguish noise points. Then, combined with the properties of level set curvature and gradient mode, the smooth region and edge region can achieve different denoising effects, so that the new model can preserve both edge and smooth noise. Experimental results show that compared with the current three mainstream models, the new model improves the peak signal to noise ratio (PSNR) by more than 1. 4 dB, reduces the mean absolute error by more than 2. 5, improves the iteration efficiency by at least 1. 6 times, and increases equally the structural similarity by 0. 13, which is more beneficial to practical application.

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