We present a novel tomographic non-local-means based despeckling technique, TNode, for optical coherence tomography. TNode is built upon a weighting similarity criterion derived for speckle in a three-dimensional similarity window. We present an implementation using a two-dimensional search window, enabling the despeckling of volumes in the presence of motion artifacts, and an implementation using a three-dimensional window with improved performance in motion-free volumes. We show that our technique provides effective speckle reduction, comparable with B-scan compounding or out-of-plane averaging, while preserving isotropic resolution, even to the level of speckle-sized structures. We demonstrate its superior despeckling performance in a phantom data set, and in an ophthalmic data set we show that small, speckle-sized retinal vessels are clearly preserved in intensity images and in two orthogonal, cross-sectional views. TNode does not rely on dictionaries or segmentation and therefore can readily be applied to arbitrary optical coherence tomography volumes. We show that despeckled esophageal volumes exhibit improved image quality and detail, even in the presence of significant motion artifacts.

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We present a Shack-Hartmann (SH) centroid detection algorithm capable to measure in presence of strong noise, background illumination and spot modulating signals, which are typical limiting factors of traditional centroid detection algorithms. The proposed method is based on performing a normalization of the SH pattern using the spiral phase transform method and Fourier filtering. The spot centroids are then obtained using global thresholding and weighted average methods. We have tested the algorithm with simulations and experimental data obtaining satisfactory results. A complete MATLAB package that can reproduce all the results can be downloaded from [http://goo.gl/o2JhD].

We demonstrate a method to easily and quickly extend the dynamic range imaging capabilities of the camera in a typical interferometric approach. The camera dynamic range is usually low and limited to 256 gray levels. Also, it is well known that one may have over or under-exposed regions in the interferogram (due to nonuniform illumination) which makes these image regions not reliable. In our proposed method it is not necessary to obtain or use the non-linear camera response curve in order to extend the camera dynamic range. We obtain a sequence of differently exposed interferograms, typically five or six; after that, we compute the corresponding normalized fringe patterns and modulation maps using a typical normalization method. These normalized patterns are combined through a temporal weighted average using as weights the corresponding modulation maps. We show a set of experimental results that prove the effectiveness of the proposed method.

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