Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding

Abstract

Mitigating the effects of the twin image artifact is one of the key challenges in holographic lens-free microscopy. This artifact arises due to the fact that imaging detectors can only record the magnitude of the hologram wavefront but not the phase. Prior work addresses this problem by attempting to simultaneously estimate the missing phase and reconstruct an image of the object specimen. Here we explore a fundamentally different approach based on post-processing the reconstructed image using sparse dictionary learning and coding techniques originally developed for processing conventional images. First, a dictionary of atoms representing characteristics from either the true image of the specimen or the twin image are learned from a collection of patches of the observed images. Then, by expressing each patch of the observed image as a sparse linear combination of the dictionary atoms, the observed image is decomposed into a component that corresponds to the true image and another one that corresponds to the twin image artifact. Experiments on counting red blood cells demonstrate the effectiveness of the proposed approach.