基于循环生成对抗网络的超分辨率重建算法研究

为了提高图像超分辨率重建的效果，该文将注意力机制引入多级残差网络(Multi-level Residual Attention Network, MRAN)作为CycleGAN的重建网络，提出了基于循环生成对抗网络(CycleGAN)的超分辨率重建模型MRA-GAN。MRA-GAN模型中重建网络负责将低分辨率(LR)图像重建为高分辨率(HR)图像，退化网络负责将HR图像降采样为LR图像，LR判别器负责鉴别真实LR图像和通过退化网络降采样得到的LR图像，HR判别器负责鉴别真实HR图像和通过重建网络重建得到的HR图像，并且改进了CycleGAN原有的判别器判别方式和损失函数。实验结果验证了MRA-GAN模型与现有算法相比，在峰值信噪比(PSNR)和结构相似性(SSIM)等指标上都有所改进。

Research on Cyclic Generation Countermeasure Network Based Super-resolution Image Reconstruction Algorithm

In order to improve the effect of image super-resolution reconstruction, the attention mechanism is introduced into Multi-level Residual Attention Network (MRAN) as the improved reconstruction network of Cycle Generation Countermeasure Network (CycleGAN) in this paper. A super-resolution reconstruction model MRA-GAN based on CycleGAN is proposed. The designed reconstruction network in MRA-GAN model is responsible for mapping from Low Resolution (LR) image to High Resolution (HR) image and the designed degradation network is responsible for reconstructing HR image to LR image. The LR discriminator is used to identify the real LR image which is obtained through the degraded network. The HR discriminator is used to identify the real HR image which is reconstructed by the reconstructed network. Moreover, the original discriminator and loss function of CycleGAN is improved. Experimental results verify that MRA-GAN model can obtain better Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) than the existing deep learning based super-resolution algorithms.