Learning Based Super Resolution

Learning-based super-resolution (SR) aims to reconstruct high-resolution images from low-resolution counterparts using machine learning, primarily deep neural networks. Current research focuses on improving model efficiency for real-time applications on mobile devices, developing self-supervised or semi-supervised training methods to reduce reliance on large, paired datasets, and incorporating physics-based models of image degradation for more realistic training and improved generalization to real-world scenarios. These advancements hold significant potential for various fields, including biomedical imaging, agricultural monitoring, and fluid dynamics analysis, by enabling higher-resolution data analysis from limited resources.