Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images. While high-quality image acquisition is typically expensive and time-consuming, medium-quality images are faster to acquire, at lower equipment costs, and available in larger quantities. Thus, we propose a novel generative adversarial network (GAN) that can leverage training data at multiple levels of quality (e.g., high and medium quality) to improve performance while limiting costs of data curation. We apply our mixed-supervision GAN to (i) super-resolve histopathology images and (ii) enhance laparoscopy images by combining super-resolution and surgical smoke removal. Results on large clinical and pre-clinical datasets show the benefits of our mixed-supervision GAN over the state of the art.
翻译:提高图像质量的深神经网络通常需要大量高精密的培训数据,包括低质量图像及其相应的高质量图像。虽然高质量图像的获取通常费用昂贵且耗时,但中质量图像的获取速度较快,设备成本较低,且数量较多。因此,我们提议建立一个新型的基因对抗网络(GAN),利用多种质量(例如高质量和中质量)的培训数据提高性能,同时限制数据整理的成本。我们将混合监督GAN应用到(i) 超溶性组织病理学图像,(ii) 通过合并超分辨率和外科烟雾清除,加强腹腔镜图像。大型临床和临床前数据集显示我们混合监督GAN对艺术状态的好处。