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.
This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces.
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
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.
Many different schemes have been proposed for the restoration of intensity 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.
Image restoration methods aim to recover the underlying clean image from corrupted observations.