Neural networks hold the key to simpler sampling for snapshot compressive imaging

An international group of researchers led by Minghao Hu of the University of Arizona in Tucson and Zongliang Wu of Westlake University in Hangzhou conducted experiments in multidimensional computational imaging to compare classic snapshot compressive imaging systems with their proposed system, which involves multiscale manifold sampling and neural decompression. They found that their method achieves comparable image quality, and holds strong promise due to expected advancements in artificial neural network processing capabilities.

arizona computational computer sciences imaging international manifold multidimensional networks neural networks researchers sampling systems the key university