FAJSCC is a new deepJSCC architecture for images that achieves better transmission performance with lower complexity than prior models and enables independent encoder-decoder compute adjustment.
Image quality assessment: from error visibility to structural similarity,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
MARMamba is a streamlined UNet with MS-Mamba modules that removes metal artifacts from CT images while preserving anatomical structures and using fewer resources.
citing papers explorer
-
Feature Importance-Aware Deep Joint Source-Channel Coding for Computationally Efficient and Adjustable Image Transmission
FAJSCC is a new deepJSCC architecture for images that achieves better transmission performance with lower complexity than prior models and enables independent encoder-decoder compute adjustment.
-
Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
MARMamba is a streamlined UNet with MS-Mamba modules that removes metal artifacts from CT images while preserving anatomical structures and using fewer resources.