Implicit Neural Speckle Denoising
Speckle fundamentally limits coherent imaging by introducing multiplicative, spatially correlated noise that obscures scene structure. Removing speckle noise from dynamic scenes--that do not benefit from conventional speckle averaging--is particularly challenging. We introduce a training-free framework that combines a spatiotemporal implicit neural representation with an aperture-aware maximum-likelihood formulation to recover dynamic, speckle-free imagery directly from noisy observations. The coherent likelihood explicitly models the aperture-dependent spatial covariance of speckle, enabling adaptation to arbitrary pupil geometries without retraining. A matrix-free implementation based o...
| Authors | Matthew R. Ziemann, Casey J. Pellizzari, Tyler J. Hardy et al. |
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| Primary category | eess.IV |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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