基于生成对抗网络的图像清晰度提升方法

视频监控、军事目标识别以及消费型摄影等众多领域对图像清晰度有很高的要求.近年来,深度神经网络在视觉和定量评估的应用研究中取得较大进展,但是其结果一般缺乏图像纹理的细节,边缘过度平滑,给人一种模糊的视觉体验.本文提出了一种基于生成对抗网络的图像清晰度提升方法.为了更好的传递图像的细节信息,采用改进的残差块和跳跃连接作为生...

Improved Image Sharpness Method Based on Generative Adversarial Network

Video surveillance, military object recognition, consumer photography, and many other fields have high requirements for image sharpness. In recent years, deep neural networks have made great progress in the applied research on visual and quantitative evaluation, but the results generally lack the details of image textures, and the edges are too smooth, providing blurry visual experience. For this reason, we propose a method of improving image sharpness based on the generative adversarial network in this study. In order to better delivery the image details, this method adopts the improved residual block and skip connection as the main structure of the generative network, and the generator loss function consists of content loss, perception loss, and texture loss in addition to adversarial loss. Finally, the experiments on the DIV2K dataset prove that the proposed method exhibits good visual experience and quantitative evaluation in terms of improving image sharpness.