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.
Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening
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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.