Synthesized Image

Synthesized images, generated by algorithms rather than captured directly, are increasingly important for various applications, primarily aiming to create realistic and high-quality images for tasks like medical image augmentation, virtual try-ons, and benchmarking large vision-language models. Current research focuses on improving the realism and controllability of synthesized images using generative models such as diffusion models, GANs, and variational autoencoders, often incorporating techniques like conditional generation and fine-grained control mechanisms. This field significantly impacts diverse areas, including healthcare (through data augmentation and improved diagnostics), art and design (through novel creative tools), and computer vision (through improved model training and evaluation).