Optical coherence tomography is an emerging non-invasive technology that provides high resolution, cross-sectional
tomographic images of internal structures of specimens. It holds great potentials for a wide variety of applications,
especially in the field of biomedical imaging. OCT images, however, are usually degraded by significant speckle noise.
Here we report a 3D approach to attenuating speckle noise in OCT images. This approach is based on the 3D curvelet
transform, and is conveniently controlled by a single parameter that determines the threshold in the curvelet domain.
Unlike 2D approaches which only consider information in individual images, 3D processing, by analyzing all images in
a volume simultaneously, has the advantage of also taking the information between images into account. This, coupled
with the curvelet transform's nearly optimal sparse representation of curved edges that are common in OCT images,
provides a simple yet powerful platform for speckle attenuation. We show the approach suppresses a significant amount
of speckle noise, and in the mean time preserves and thus reveals many subtle features that could get attenuated in other approaches.