5 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Scalable neural pushbroom architectures for real-time denoising of hyperspectral images onboard satellites
Ziyao Yi, Davide Piccinini, Diego Valsesia +2
The next generation of Earth observation satellites will seek to deploy intelligent models directly onboard the payload in order to minimize the latency incurred by the transmissio…
A low-complexity method for efficient depth-guided image deblurring
Ziyao Yi, Diego Valsesia, Tiziano Bianchi +1
Image deblurring is a challenging problem in imaging due to its highly ill-posed nature. Deep learning models have shown great success in tackling this problem but the quest for th…
Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network
Davide Piccinini, Diego Valsesia, Enrico Magli
Hyperspectral imagers on satellites obtain the fine spectral signatures essential for distinguishing one material from another at the expense of limited spatial resolution. Enhanci…
Efficient onboard multi-task AI architecture based on self-supervised learning
Gabriele Inzerillo, Diego Valsesia, Enrico Magli
There is growing interest towards the use of AI directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This paper presents…
Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Diego Valsesia, Tiziano Bianchi, Enrico Magli
Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to…