Removing high contrast artifacts via digital inpainting in cryo-electron tomography: an application of compressed sensing

Abstract

To cope with poor quality in cryo-electron tomography images, electron-dense markers, such as colloidal goldbeads, are often used to assist image registration and analysis algorithms. However, these markers can create artifacts that occlude a specimen due to their high contrast, which can also cause failure of some image processing algorithms. One way of reducing these artifacts is to replace high contrast objects with pixel densities that blend into the surroundings in the projection domain before volume reconstruction. In this paper, we propose digital inpainting via compressed sensing (CS) as a new method to achieve this goal. We show that cryo-ET projections are sparse in the discrete cosine transform (DCT) domain, and, by finding the sparsest DCT domain decompositions given uncorrupted pixels, we can fill in the missing pixel values that are occluded by high contrast objects without discontinuities. Our method reduces visual artifacts both in projections and in tomograms better than conventional algorithms, such as polynomial interpolation and random noise inpainting.