基于生成对抗网络的多幅离焦图像超分辨率重建算法

为提高超分辨率算法重建出的图像质量，提出融合多幅离焦图像的超分辨率重建算法。以离焦图像作为切入点，利用自编码器提取离焦图像中的重要特征，根据空间特征变换层结构，将离焦特征与原始特征相结合，完成图像的超分辨率重建。在Celeb A人脸数据集上进行实验，结果表明，与传统插值算法及SRGAN算法相比，所提算法在大多数情况下能获得更高峰值信噪比及结构相似性数值，能生成质量更高的重建图像。

Super-Resolution Reconstruction Algorithm with Multi-Frame Defocused Images Based on Generative Adversarial Network

In order to improve the quality of the reconstructed images, a super-resolution reconstruction algorithm using multi-frame defocused images is proposed.The algorithm employs an auto-encoder to extract the important features in the defocused images, and the layer structure is transformed based on spatial features to combine the defocused features with the original features, so the super-resolution reconstruction of the image is completed.The experimental results on the Celeb A face data set show that in most cases, the proposed algorithm provides a higher peak signal-to-noise ratio and structural similarity than the traditional interpolation algorithm and the SRGAN algorithm.This super-resolution algorithm based on multi-frame defocused images can generate better reconstructed images.