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Found 5 papers, 2 papers with code
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be significantly more effective than its single-image counterpart.
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms.
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data.
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency.
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy.
Ranked #8 on
Multi-Frame Super-Resolution
on PROBA-V
