Fast and Flexible Blind Deep Denoising Model with Noise Level Estimation
摘要
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.