Iterative Bayesian denoising based on variance stabilization using Contourlet Transform with Sharp Frequency Localization: application to EFTEM images.

Background: Due to the presence of high noise level in tomographic series of energy filtered transmission electron microscopy (EFTEM) images, alignment and 3D reconstruction steps become so difficult. To improve the alignment process which will in turn allow a more accurate and better three dimensional tomography reconstructions, a preprocessing step should be applied to the EFTEM data series.

Results: Experiments with real EFTEM data series at low SNR, show the feasibility and the accuracy of the proposed denoising approach being competitive with the best existing methods for Poisson image denoising.

Iterative Variance Stabilizing Transformation Denoising of Spectral Domain Optical Coherence Tomography Images Applied to Retinoblastoma.

Background: Due to the presence of speckle Poisson noise, the interpretation of spectral domain-optical coherence tomography (SD-OCT) images frequently requires the use of data averaging to improve the signal-to-noise ratio. This implies long acquisition times and requires patient sedation in some cases. Iterative variance stabilizing transformation (VST) is a possible approach by which to remove speckle Poisson noise on single images.