Image Harmonization Task

Image harmonization aims to seamlessly integrate foreground elements into a background image, achieving visual consistency. Recent research focuses on leveraging deep learning models, particularly generative adversarial networks (GANs) and diffusion models, to address this image-to-image translation problem, with a strong emphasis on preserving anatomical details in medical imaging and mitigating artifacts. These advancements are improving the quality and realism of harmonized images, impacting applications such as medical image analysis, where harmonization enhances data consistency across different scanners or settings, and computer vision tasks like image editing and object insertion. The field is also exploring techniques to handle multiple foreground regions and complex shading effects for more robust and versatile harmonization.