A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.
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A new AMC architecture with cross-channel self-attention and feature-preserving denoising achieves 3-14% higher accuracy than benchmarks at low-to-medium SNRs on the RML2018.01a dataset.
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Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising
A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.
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Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification
A new AMC architecture with cross-channel self-attention and feature-preserving denoising achieves 3-14% higher accuracy than benchmarks at low-to-medium SNRs on the RML2018.01a dataset.