Python计算机课程设计项目:基于改进UNet和GAN的图像修复系统

上传者: csdn1561168266 | 上传时间: 2025-06-16 10:10:08 | 文件大小: 56.08MB | 文件类型: ZIP
基于生成对抗网络(GAN)的图像修复算法,旨在通过利用深度学习技术修复图像中的缺陷和损坏区域。算法中包括两个主要组件:一个生成器(Generator)和一个判别器(Discriminator)。生成器使用的是无注意力机制的全卷积架构UNet,而判别器采用的是PatchGAN架构。预处理过程中,加载图像和掩码文件并调整大小,进行随机掩码应用,准备模型输入。生成器根据对抗损失、感知损失和结构一致性损失调整其参数,以改善生成图像的质量和真实性。判别器评估两类图像:真实的未损坏图像和生成器产生的修复图像。通过设计生成器和判别器,算法能够有效地处理和修复图像中的缺陷。

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