Multiplane Image Restoration Using Multivariate Curve Resolution: An Alternative Approach to Deconvolution in Conventional Brightfield Microscopy

Abstract

Three-dimensional reconstruction in brightfield microscopy is challenging since a 2D image includes from in-focus and out-of-focus light which removes the details of the specimen’s structures. To overcome this problem, many techniques exist, but these generally require an appropriate model of Point Spread Function (PSF). Here, we propose a new images restoration method based on the application of Multivariate Curve Resolution (MCR) algorithms to a stack of brightfield microscopy images to achieve 3D reconstruction without the need for PSF. The method is based on a statistical reconstruction approach using a self-modelling mixture analysis. The MCR-ALS (ALS for Alternating Least Square) algorithm under non-negativity constraints, Wiener, Richardson–Lucy, and blind deconvolution algorithms were applied to silica microbeads and red blood cells images. The MCR analysis produces restored images that show informative structures which are not noticeable in the initial images, and this demonstrates its capability for the multiplane reconstruction of the amplitude of 3D objects. In comparison with 3D deconvolution methods based on a set of No Reference Images Quality Metrics (NR-IQMs) that are Standard Deviation, ENTROPY Average Gradient, and Auto Correlation, our method presents better values of these metrics, showing that it can be used as an alternative to 3D deconvolution methods.