发表机构
DEVCOM Army Research Laboratory; University of Maryland; United States Air Force Academy（DEVCOM陆军研究实验室; 马里兰大学; 美国空军学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无需训练的时空隐式神经表示结合感知孔径最大似然公式的框架，可从含噪观测恢复动态无散斑图像，在多方面优于现有基线方法。
AI 中文摘要
散斑作为乘性、空间相关的噪声，从根本上限制了相干成像，会掩盖场景结构。动态场景无法受益于传统散斑平均方法，去除其散斑噪声尤为具有挑战性。本文提出一种无需训练的框架，结合时空隐式神经表示与感知孔径的最大似然公式，可直接从含噪观测中恢复动态无散斑图像。该相干似然明确建模散斑的孔径依赖空间协方差，无需重新训练即可适配任意光瞳几何。基于FFT加速算子、随机近似与共轭梯度的无矩阵实现，使优化适用于实际图像尺寸；同时，盲保留准则可在无干净参考数据时自动早停。模拟与实验结果表明，与经典、无监督及有监督基线相比，该方法在空间保真度、时间一致性及对不同散斑统计的鲁棒性上均有提升。
英文摘要
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 on FFT-accelerated operators, stochastic approximations, and conjugate gradients makes optimization practical for realistic image sizes. Meanwhile, a blind holdout criterion provides automatic early stopping without clean reference data. Simulated and laboratory results demonstrate improved spatial fidelity, temporal consistency, and robustness to varying speckle statistics relative to classical, unsupervised, and supervised baselines.
Commentscomments: 9 pages, 9 figures; supplemental document included
