In this paper, we propose a bidirectional learning model, known as dual contrast cycleGAN (DC-cycleGAN), for medical image synthesis from unpaired data. Specifically, a dual contrast (DC) loss is formulated that
The training set of DC-CycleGAN includes two, trainA and trainB, and the test set also includes two, testA and testB. However, during the restoration process, we found that if we use original mural images, i.e., faded or damaged mural images, the problem of inadequate feature extraction...
为了实现这样的转换,CycleGAN使用了两个生成器和两个判别器,以及一组损失函数,其中最重要的损失函数有四个:生成器损失、判别器损失、循环一致性损失和身份损失。 1.生成器损失: 生成器的任务是将一个域中的图像转换为另一个域中的图像。生成器损失量化了生成器的转换质量,帮助生成器学习将输入图像转换为逼真的...
基于CycleGAN生成对抗网络的跨域车辆检测方法专利信息由爱企查专利频道提供,基于CycleGAN生成对抗网络的跨域车辆检测方法说明:本发明涉及一种基于CycleGAN生成对抗网络的跨域车辆检测方法,包括采集白天场景和黑夜场景下...专利查询请上爱企查
CycleGAN and its variants are widely used in medical image synthesis, which can use unpaired data for medical image synthesis. The most commonly used method is to use a Generative Adversarial Network (GAN) model to process 2D slices and thereafter concatenate all of these slices to 3D medical ...