AnyBand-Diff is a spectral-prior-guided diffusion model that unifies remote sensing image generation and band repair while maintaining radiometric fidelity through physics-guided sampling and multi-scale losses.
Advances in Neural Information Processing Systems , volume=
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Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
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AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
AnyBand-Diff is a spectral-prior-guided diffusion model that unifies remote sensing image generation and band repair while maintaining radiometric fidelity through physics-guided sampling and multi-scale losses.
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Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.