Real World Image Super Resolution

Real-world image super-resolution (Real-ISR) aims to enhance the resolution of low-resolution images degraded by complex, unknown processes, unlike simpler synthetic scenarios. Current research heavily utilizes generative adversarial networks (GANs) and diffusion models, with a focus on improving efficiency (e.g., one-step methods) and addressing artifacts through techniques like self-similarity losses and degradation-aware modules. These advancements are significant because they improve the quality and realism of upscaled images, impacting applications ranging from medical imaging to surveillance and consumer photography. The field is also exploring self-supervised and few-shot learning approaches to mitigate the need for large, paired datasets.