Image synthesis is the process of generating artificial images using computational techniques, often powered by machine learning and deep learning models. This field has advanced significantly with the introduction of Generative Adversarial Networks (GANs) in 2014, which use adversarial training to create realistic and detailed images from random noise or structured inputs. A key application of image synthesis is in creating photorealistic visuals for industries such as entertainment and advertising. For example, platforms like NVIDIA's GauGAN, introduced in 2019, allow users to generate landscapes from simple sketches.
https://en.wikipedia.org/wiki/Image_synthesis
Modern image synthesis techniques leverage neural networks like StyleGAN (introduced in 2018) to produce high-quality, high-resolution images with controllable attributes. Conditional GANs (cGANs) are another extension that enables targeted image generation based on specific inputs, such as generating faces with desired attributes or transforming textual descriptions into images. These advancements have found applications in creative design, such as generating concept art, and in data augmentation, where synthetic images enhance training datasets for other AI models.
https://en.wikipedia.org/wiki/StyleGAN
In addition to creative and commercial applications, image synthesis is utilized in scientific research and healthcare. For instance, synthetic medical images are used to train diagnostic systems, reducing the need for large annotated datasets. Image-to-image translation techniques, such as those implemented in CycleGAN, allow transformations like converting day scenes to night or sketches to photorealistic images. Despite these advancements, challenges remain in ensuring the ethical use of synthesized images, such as preventing misuse in deepfakes or misinformation campaigns, highlighting the importance of developing detection and regulation tools.