Image-to-image translation is a technique that focuses on transferring images from one domain to another while maintaining the essential content representations. In recent years, image-to-image translation has gained significant attention and achieved remarkable advancements due to its diverse applications in computer vision and image processing tasks. In this work, we propose an innovative method for image translation between different domains. For high-resolution image translation tasks, we use a grayscale adjustment method to achieve pixel-level translation. For other tasks, we utilize the Pix2PixHD model with a coarse-to-fine generator, multi-scale discriminator, and improved loss to enhance the image translation performance. On the other hand, to tackle the issue of sparse training data, we adopt model weight initialization from other task to optimize the performance of the current task.

翻译：图像到图像翻译是一种专注于将图像从一个域转换到另一个域并保持关键内容表示的技术。近年来，由于其在计算机视觉和图像处理任务中的多样化应用，图像到图像翻译引起了广泛关注并取得了显著进展。在这项工作中，我们提出了一种创新的图像在不同域之间翻译的方法。对于高分辨率图像翻译任务，我们采用灰度调整方法实现像素级翻译。对于其他任务，我们利用具有从粗到细生成器、多尺度判别器及改进损失函数的Pix2PixHD模型来提升图像翻译性能。此外，为解决训练数据稀疏的问题，我们从其他任务中采用模型权重初始化方法来优化当前任务的性能。