  Optical coherence tomography (OCT) is a prevalent, interferometric,
high-resolution imaging method with broad biomedical applications. Nonetheless,
OCT images suffer from an artifact, called speckle which degrades the image
quality. Digital filters offer an opportunity for image improvement in clinical
OCT devices where hardware modification to enhance images is expensive. To
reduce speckle, a wide variety of digital filters have been proposed, selecting
the most appropriate filter for each OCT image/image set is a challenging
decision. To tackle this challenge, we propose an expandable learnable
despeckling framework, we called LDF. LDF decides which speckle reduction
algorithm is most effective on a given image by learning a figure of merit
(FOM) as a single quantitative image assessment measure. The architecture of
LDF includes two main parts: (i) an autoencoder neural network, (ii) filter
classifier. The autoencoder learns the figure of merit based on the quality
assessment measures obtained from the OCT image including.Subsequently, the
filter classifier identifies the most efficient filter from the following
categories: (a) sliding window filters including median, mean, and symmetric
nearest neighborhood, (b) adaptive statistical based filters including Wiener,
homomorphic Lee, and Kuwahara, and, (c) edge preserved patch or pixel
correlation based filters including non-local mean, total variation, and block
matching 3D filtering.
