  Speckle noise is an inherent disturbance in coherent imaging systems such as
digital holography, synthetic aperture radar, optical coherence tomography, or
ultrasound systems. These systems usually produce only single observation per
view angle of the same interest object, imposing the difficulty to leverage the
statistic among observations. We propose a novel image restoration algorithm
that can perform speckle noise removal without clean data and does not require
multiple noisy observations in the same view angle. Our proposed method can
also be applied to the situation without knowing the noise distribution as
prior. We demonstrate our method is especially well-suited for spectral images
by first validating on the synthetic dataset, and also applied on real-world
digital holography samples. The results are superior in both quantitative
measurement and visual inspection compared to several widely applied baselines.
Our method even shows promising results across different speckle noise
strengths, without the clean data needed.
