• 图形图像处理 •

### 带噪声水平评估的快速灵活盲深度降噪模型

1. 南昌大学 信息工程学院, 南昌 330031
• 收稿日期:2020-05-14 修回日期:2020-06-15 发布日期:2020-06-22
• 作者简介:于海雯(1972-),女,讲师、硕士,主研方向为图形图像处理技术、机器视觉;易昕炜,本科生;徐少平(通信作者),教授、博士生导师;林珍玉,硕士研究生。
• 基金项目:
国家自然科学基金（61662044，61163023）；江西省自然科学基金（20171BAB202017）。

### Fast and Flexible Blind Deep Denoising Model with Noise Level Estimation

YU Haiwen, YI Xinwei, XU Shaoping, LIN Zhenyu

1. School of Information Engineering, Nanchang University, Nanchang 330031, China
• Received:2020-05-14 Revised:2020-06-15 Published:2020-06-22

Abstract: To improve the denoising performance of the Fast and Flexible Denoising Convolutional Neural Network(FFDNet),this paper proposes a Noise Level Estimation(NLE) model that estimates the level of noise.The estimation result is input into the FFDNet model,and the NLE model is taken as the preceding module of the FFDNet deep denoising model to transform it into a blind denoising model.Then the shallow convolutional neural network model is used to separate noise signals from noisy images to obtain the noise map,the standard deviation of which is taken as the initial estimated value of the noise level.Considering the fact that there exists strong correlation between the initial estimated value and ground-truths of the noise level,a Back-Propagation(BP) neural network model is used to correct the initial estimated value of noise level.Experimental results show that when the proposed NLE model works with the FFDNet model,its denoising performance is close to that of the FFDNet denoising model which uses the ground-truths of noise level.For most of the noise level values,the difference of Peak Signal to Noise Ratio(PSNR) values between the two models is within 0.1 dB,which means the estimation results of the proposed NLE model are similar to the ground-truths of noise level,bringing the fast and flexible characteristics of the FFDNet model into full play.