RAFNet uses wavelet-based directional separation, K-means regional clustering, and clustered sparse attention to create adaptive kernels and efficient frequency aggregation, outperforming prior pansharpening networks on benchmark datasets.
Efficient language modeling with sparse all-mlp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
RAFNet: Region-Aware Fusion Network for Pansharpening
RAFNet uses wavelet-based directional separation, K-means regional clustering, and clustered sparse attention to create adaptive kernels and efficient frequency aggregation, outperforming prior pansharpening networks on benchmark datasets.