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Found 9 papers, 5 papers with code
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs.
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data.
Multi-temporal speckle reduction with self-supervised deep neural networks
Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images.
As if by magic: self-supervised training of deep despeckling networks with MERLIN
We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks.
Image Restoration for Remote Sensing: Overview and Toolbox
Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community.
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters.
