面向图像场景转换的改进型生成对抗网络

设计了新的生成器网络、判决器网络以及新的损失函数，用于图像场景转换.首先，生成器网络采用了带跨层连接结构的深度卷积神经网络，其中，多个跨层连接以实现图像结构信息的共享；而判决器网络采用了多尺度全域卷积网络，多尺度判决器可以区分不同尺寸下的真实和生成图像.同时，对于损失函数，该算法借鉴其他算法提出了4种损失函数的组合，并通过实验对比证明了新损失函数的有效性，包括GAN损失、L₁损失、VGG损失、FM损失.从实验结果显示，该算法能够实现多种转换，且转换后图像的细节保留较为完整，生成图像较为真实，明显消除了块效应.

Improved Generative Adversarial Network for Image Scene Transformation

This study designs a new generator network, a new discriminator network, and a new loss function for image scene conversion. First, the generator network uses a deep convolutional neural network with a skip connection structure, in which multi-skip connection is used to share the structure information of the image. For the discriminator network, it uses a multi-scale global convolutional network which can distinguish between real and generated images of different sizes. At the same time, the new loss function is a combination of four loss functions referring to other algorithms, including GAN loss, L₁ loss, VGG loss, and feature matching loss. Moreover, the validity of the new loss function is demonstrated through experimental comparisons. The experimental results show that the proposed algorithm can achieve multi-image transformations, and the details of generated images are preserved completely, the generated image is more realistic, and the block effect is obviously eliminated.