Coarse Resolution

Coarse resolution in scientific data presents challenges in accurately capturing fine-grained details crucial for many applications. Current research focuses on developing methods to enhance resolution, often employing deep learning architectures like U-Nets, ConvLSTMs, and Vision Transformers, sometimes augmented with physics-informed constraints or stochastic resonance techniques. These advancements aim to improve the accuracy of simulations, predictions (e.g., rainfall forecasting, haze prediction), and analyses (e.g., change detection in remote sensing, material clustering in hyperspectral images) while mitigating computational costs. Ultimately, overcoming coarse resolution limitations leads to more precise and reliable insights across diverse scientific fields.