High Resolution
High-resolution imaging and data processing are crucial for advancing numerous scientific fields, aiming to improve accuracy and detail in various applications. Current research focuses on developing and applying deep learning models, including diffusion models, transformers, and graph neural networks, to enhance resolution in diverse data types such as images, videos, and sensor readings. This work is significantly impacting fields ranging from weather forecasting and medical imaging to remote sensing and autonomous driving, enabling more precise analyses and improved decision-making. The development of high-resolution datasets and benchmark evaluations is also a key focus, facilitating the comparison and improvement of these advanced models.
Papers
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance
Ruxin Zheng, Shunqiao Sun, Holger Caesar, Honglei Chen, Jian Li
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
Athanasios Tragakis, Marco Aversa, Chaitanya Kaul, Roderick Murray-Smith, Daniele Faccio