Benefiting from the excellent texture recovery capability of generative adversarial networks
(GANs), generated images are capable of maintaining clear texture features even when …

Compressing remote sensing images with high spatial and spectral resolution plays an
important role in subsequent image processing and information acquisition. Accurate data …

In recent years, the impressive feature representation capabilities of deep learning have
opened up new possibilities for image compression. Most of the existing learning-based …

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing.
However, there is a limited body of literature specifically addressing the compression of SAR …

Due to its inherent characteristics, the synthetic aperture radar (SAR) image is mainly
corrupted by speckle noise, posing additional challenges to lossy image compression …

Synthetic aperture radar (SAR) images are pivotal in remote sensing applications. However,
due to the physical characteristics of coherent imaging, current SAR image compression …

Synthetic Aperture Radar (SAR) is an active and coherent imaging system that utilizes radio
waves to illuminate the Earth's surface, generating complex-valued images of the ground. It …

Compression of Synthetic Aperture Radar (SAR) images presents significant challenges due
to inherent noise in the data acquisition process. SAR images consist of two components:“ …

Image representation has an important role in the fields of computer vision, artificial
intelligence and medical imaging. In this paper, based on the research of the existing image …

Synthetic Aperture Radar (SAR) images are usually corrupted by speckle noise, which
introduces additional challenges to lossy image compression algorithms. Traditional optical …