基于生成对抗网络与噪声分布的图像超分辨率重建方法

现有的图像超分辨率重建方法都较少考虑真实低分辨率图像中包含的噪声信息，因此会影响图像的重建质量.受真实图像去噪算法的启发，本文引入一个噪声分布收集网络来收集低分辨率图像的噪声分布信息，并采用生成对抗网络的模型设计，提高含噪声图像的重建质量.噪声分布信息会分别输入到超分辨率重建网络和判别网络，在重建过程中去除噪声的同时保证有用高频信息的恢复，另外由于判别网络的能力对整个模型的性能有着重要影响，选择使用 U-Net 网络来获得更好的梯度信息反馈.与经典图像超分辨率重建算法的对比以及消融实验表明，使用噪声收集网络和 U-Net 判别网络后，本文模型在噪声低分辨率图像重建任务中获得了更好的性能.

Super-resolution of images based on Generative Adversarial Network and noise distribution

Existing image super-resolution reconstruction methods take less into account the noise information contained in real low-resolution images, which will affect the quality of image reconstruction. Inspired by the real image denoising algorithm, this paper introduces a noise distribution collection network to collect noise distribution information of low-resolution images, and adopts a model design of Generative Adversarial Network to improve the reconstruction quality of noisy images. The noise distribution information will be input to the super-resolution reconstruction network and the discriminant network respectively. During the reconstruction process, the noise is removed during while ensuring the recovery of useful high-frequency information, because the ability of the discriminant network has an important impact on the performance of the entire model, the U-Net network is selected to obtain better gradient information feedback. Comparison with the classical image super-resolution reconstruction methods and ablation experiments,the resluts show that the proposed model obtains better performance in the noisy low-resolution image reconstruction task after using the noise collection network and the U-Net discriminant network.