Image to Image

Image-to-image translation aims to transform images from one domain to another, encompassing tasks like style transfer, medical image conversion, and super-resolution. Current research emphasizes developing efficient and versatile models, often leveraging generative adversarial networks (GANs), diffusion models, and increasingly, foundation models incorporating both image and text information for more flexible control. This field is significant for its applications in diverse areas, including healthcare, remote sensing, and creative arts, with ongoing efforts focused on improving image quality, handling unpaired data, and mitigating issues like bias and backdoor vulnerabilities.