Limited Field

"Limited field" research encompasses diverse challenges arising from restricted data acquisition or processing capabilities, impacting various scientific domains. Current efforts focus on improving data efficiency through techniques like cross-field information utilization in lossy compression, adaptive algorithms for mitigating data limitations (e.g., in uncorrected DRAM errors or limited field-of-view sensor networks), and employing generative models and neural networks to enhance data quality and extend effective field coverage (e.g., in image super-resolution and radiance field reconstruction). These advancements are crucial for optimizing resource utilization, improving the accuracy and reliability of analyses, and enabling new applications in fields ranging from agriculture and robotics to medical imaging and cosmology.