COMPUTER ALGORITHMS FOR SYNTHETIC IMAGES MODELLING BASED ON DIFFUSION MODELS
Eugene Yu. Shchetinin · SOFT MEASUREMENTS AND COMPUTING · 2023
Deep neural networks have made significant breakthroughs in the field of medical image analysis. However, due to their high data requirements, small datasets in medical imaging tasks can hinder their capabilities. Synthetic data generation is a promising alternative to augment training datasets and enable medical imaging studies on a larger scale. Recently, deep generative models have attracted the attention of the computer vision community because they allow the generation of photorealistic synthetic images. In this paper, we investigate the potential use of deep generative models and develop computer algorithms to generate highresolution synthetic images. The obtained results of computer modelling confirmed the high efficiency and advantages of diffusion models in image synthesis problems.