no code implementations • 6 Nov 2023 • Ali Zafari, Atefeh Khoshkhahtinat, Jeremy A. Grajeda, Piyush M. Mehta, Nasser M. Nasrabadi, Laura E. Boucheron, Barbara J. Thompson, Michael S. F. Kirk, Daniel da Silva
In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs.
no code implementations • 16 May 2023 • Laura E. Boucheron, Ty Vincent, Jeremy A. Grajeda, Ellery Wuest
In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).