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
Quark: Real-time, High-resolution, and General Neural View Synthesis
John Flynn, Michael Broxton, Lukas Murmann, Lucy Chai, Matthew DuVall, Clément Godard, Kathryn Heal, Srinivas Kaza, Stephen Lombardi, Xuan Luo, Supreeth Achar, Kira Prabhu, Tiancheng Sun, Lynn Tsai, Ryan Overbeck
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training
Yiying Wei, Hadi Amirpour, Jong Hwan Ko, Christian Timmerer
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution
Yuehan Zhang, Angela Yao