Researchers have developed a minimax theory for likelihood-based deep learning to address speckle noise in imaging modalities like synthetic aperture radar and optical coherence tomography. This new framework handles both multiplicative speckle noise and additive Gaussian noise, accommodating low-dimensional and sparse high-dimensional features. The study establishes finite-sample upper bounds and derives minimax lower bounds, showing that the intrinsic difficulty of estimation is largely unchanged compared to models with only additive Gaussian noise. AI
IMPACT Provides a theoretical foundation for applying deep learning to challenging noise models in scientific imaging.
RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for deep learning in a specific statistical context.
- arXiv
- deep learning
- deep neural network
- Digital holography
- Gaussian noise
- Hugging Face
- Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression
- optical coherence tomography
- speckle noise
- synthetic aperture radar
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