基于无监督深度图像生成的盲降噪模型

鉴于有监督神经网络降噪模型的数据依赖缺陷，提出了一种基于无监督深度生成（UDIG）的盲降噪模型。首先，利用噪声水平评估（NLE）算法测定给定噪声图像中的噪声水平值并输入到主流FFDNet降噪模型中，所得到降噪后的图像（称为初步降噪图像）作为UDIG降噪模型的输入。其次，选用编码器—解码器架构作为UDIG模型的骨干网络并用UDIG模型的输出图像（即生成图像）分别与初步降噪图像、噪声图像之间的均方误差之和构建混合loss函数；再次，以loss最小化为优化目标，通过随机梯度下降（SGD）网络训练算法调整网络模型的参数值从而获得一系列生成图像；最后，当残差图像（噪声图像与生成图像之间）的标准差逼近之前NLE算法所测定的噪声水平估计值时及时终止网络迭代训练过程，从而确保生成图像（作为降噪后图像）的图像质量最佳。实验结果表明：与现有的主流降噪模型（算法）相比，UDIG降噪模型在降噪效果上具有显著优势。

Blind denoising model based on unsupervised deep image generation

In view of the data dependence that is the major drawback of the supervised deep neural network(DNN) -based denoising models, this paper proposed an unsupervised deep image generation(UDIG) denoising model. Firstly, it utilized a noise level estimation(NLE) algorithm to measur the noise level of a given noisy image, the estimated noise level and the noisy image fed into the state-of-the-art denoising model(i. e., fast and flexible denoising convolutional neural network, FFDNet) to obtain a preliminary denoised image as the input to the UDIG model. Seconded, it chose the encoder-decoder architecture as backbone network, while using the sum of the mean square error among the output image of UDIG model(i. e. generated image), and the preliminary denoised image, and the given noisy image to define mixed loss function. Then, the regular stochastic gradient descent(SGD) algorithm optimized the loss function, adjusted the hyper-parameters of the UDIG model and generating a series of generated images. Finally, when the standard deviation of the residual image between the noisy image and generated image approximates the noise level measured by the NLE algorithm, the network iterative process was terminated adaptively, ensuring the image quality of the generated image(treated as the denoised image). Extensive experiments show that, the proposed UDIG denoising model has a better performance than other state-of-the-art counterparts with regard to denoising effect.