基于改进生成对抗网络的红外图像增强算法

针对红外图像细节分辨率不高、目标边缘模糊等，提出一种基于改进生成对抗网络的红外图像增强算法。首先，基于编码解码网络U-Net构建生成器，优化U-Net跳跃连接方式，融合全局上下文模块，实现全局和局部特征的上下文建模；然后，基于胶囊网络构建鉴别器，结合Res2Net改进胶囊网络结构，并对胶囊网络全连接层进行反卷积重构，实现多尺度图像特征提取，减少模型参数冗余。实验表明，与当前主流算法相比，该算法能有效突出细节信息、抑制噪声，提高图像分辨率和视觉效果。

Infrared Image Enhancement Algorithm Based on Improved Generative Adversarial Network

In order to address the problems of poor detail resolution and blurred target edges in infrared images, an image enhancement method based on improved Generative Adversarial Network is proposed. Firstly, the generator was constructed based on the codec network U-Net, optimizing the U-Net skip connection method and fusing the global context module to achieve contextual modelling of global and local features. Secondly, the discriminator is constructed based on the Capsule Networks, the capsule network structure is improved by combining with Res2Net structure and the fully connected layer of the Capsule Networks was deconvolutionally reconfigured to achieve multi-scale image feature extraction and reduce model parameters redundancy. The experimental results show that, compared with the current mainstream algorithms, the algorithm in this paper can effectively highlight the detail information, suppress the noise, and improve the image resolution and visual effect.