Super resolution generation, diffusion model, deep learning
Abstract
In recent years, diffusion models have emerged as a powerful tool in the field of machine learning, particularly for high-resolution image generation. These models simulate a noise-to-data generative process, making them highly effective in producing realistic and detailed images. This paper explores the potential of diffusion models in the domain of super-resolution, where low-resolution images are transformed into high-resolution versions. While traditional methods such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) have achieved success in super-resolution tasks, they often struggle to maintain naturalness and fidelity in highly degraded input data. Diffusion models, on the other hand, offer a more robust alternative, capable of generating structurally coherent images with fine textures. However, the computational demands of these models present significant challenges, requiring advanced hardware and long processing times. This paper highlights recent advancements in diffusion models, particularly in the medical imaging and film industries, and discusses the techniques used to optimize their performance for real-world applications. Despite the challenges, diffusion models hold great promise for producing high-quality, high-resolution images, offering new possibilities in fields where precision and detail are critical, such as medical diagnostics and satellite imagery.
