Restoration of images corrupted by mixed noise (e.g., additive white Gaussian noise and impulse noise) is very difficult due to the complexity of the mixed noise distribution. Various mixed noise removal models involve the preprocessing based on outlier detection. However, the performance of these models largely depends on the accuracy of pixel location detection of outliers, and artifacts and missing image details are prone to occur when the mixture noise is strong. In this paper, a new denoising model based on generative adversarial network (DeGAN) is proposed to remove mixed noise in images. The proposed model combines generator, discriminator, and feature extractor networks. Through the mutual game between the generator and discriminator networks combined with additional training from the feature extractor network, the generator network implements a direct mapping from the noisy image domain to the noise-free image domain. In addition, we design a new joint loss function to incorporate information from image features and human visual perception into the mixed noise elimination task, which further improves the image quality and the visual effect. Abundant experiments show that the performance of our model is better than the state-of-the-art mixed noise removal methods in three different types of mixed noise scenarios, and the joint loss function does improve the denoising performance.

由于混合噪声分布的复杂性，恢复被混合噪声（例如加性高斯白噪声和脉冲噪声）破坏的图像非常困难。各种混合噪声消除模型涉及基于异常值检测的预处理。但是，这些模型的性能在很大程度上取决于离群点的像素位置检测的准确性，并且当混合噪声强时，容易出现伪影和图像细节丢失。本文提出了一种基于生成对抗网络（DeGAN）的去噪模型，以去除图像中的混合噪声。所提出的模型结合了生成器，鉴别器和特征提取器网络。通过生成器和鉴别器网络之间的相互博弈，再加上特征提取器网络的额外训练，发生器网络实现从噪声图像域到无噪声图像域的直接映射。此外，我们设计了一种新的关节损失功能，将来自图像特征和人类视觉感知的信息纳入混合噪声消除任务，从而进一步改善了图像质量和视觉效果。大量实验表明，在三种不同类型的混合噪声场景中，我们的模型的性能优于最新的混合噪声去除方法，并且联合损失函数的确改善了降噪性能。从而进一步改善了图像质量和视觉效果。大量实验表明，在三种不同类型的混合噪声场景中，我们的模型的性能优于最新的混合噪声去除方法，并且联合损失函数的确改善了降噪性能。从而进一步改善了图像质量和视觉效果。大量实验表明，在三种不同类型的混合噪声场景中，我们模型的性能优于最新的混合噪声去除方法，并且联合损失函数的确改善了降噪性能。