Additive manufacturing (AM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the AM process, it is of critical importance to obtain high-quality thermal images by using image processing techniques. In this paper, a novel image processing method is put forward with aim to improve the contrast ratio of the thermal images for image segmentation. To be specific, an image-enhancement generative adversarial network (IEGAN) is developed, where a new objective function is designed for the training process. To verify the superiority and feasibility of the proposed IEGAN, the thermal images captured from an AM process are utilized for image segmentation. Experiment results demonstrate that the developed IEGAN outperforms the original GAN in improving the contrast ratio of the thermal images.

增材制造（AM）是一种流行的制造技术，广泛用于快速原型设计和制造具有复杂几何形状的组件。为了确保AM过程的稳定性，通过使用图像处理技术获得高质量的热图像至关重要。提出了一种新颖的图像处理方法，旨在提高热图像图像分割的对比度。具体来说，开发了图像增强生成对抗网络（IEGAN），其中针对训练过程设计了新的目标功能。为了验证所提出的IEGAN的优越性和可行性，将从AM过程捕获的热图像用于图像分割。