Currently, image denoising is a challenge in many applications of computer vision. The existing denoising methods depend on the information of noise types or levels, which are generally classified by experts. These methods have not applied computational methods to pre-classify the image noise types. Furthermore, some methods assume that the noise type of the image is a certain one like Gaussian noise, which limits the ability of the denoising in real applications. Different from the existing methods, this paper introduces a new method that can classify and denoise not only a certain type noise but also mixed types of noises for real demand. Our method utilizes two types of deep learning networks. One is used to classify the noise type of the images and the other one performs denoising based on the classification result of the first one. Our framework can automatically denoise single or mixed types of noises with these efforts. Our experimental results show that our classification network achieves higher accuracy, and our denoising network can ensure higher PSNR and SSIM values than the existing methods.

当前，在计算机视觉的许多应用中，图像去噪是一个挑战。现有的降噪方法取决于噪声类型或级别的信息，这些信息通常由专家进行分类。这些方法尚未应用计算方法对图像噪声类型进行预分类。此外，一些方法假设图像的噪声类型是高斯噪声之类的某种噪声，这限制了实际应用中的去噪能力。与现有方法不同，本文介绍了一种新方法，该方法不仅可以对特定类型的噪声进行分类和去噪，还可以对实际需求的混合噪声进行分类和去噪。我们的方法利用了两种类型的深度学习网络。一个用于对图像的噪声类型进行分类，另一个用于根据第一个图像的分类结果进行降噪。通过这些努力，我们的框架可以自动对单个或混合类型的噪声进行降噪。我们的实验结果表明，与现有方法相比，我们的分类网络具有更高的准确度，而去噪网络可以确保更高的PSNR和SSIM值。