Adaptive background interference removal for Fourier ptychographic microscopy

Abstract

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging method that achieves both high resolution (HR) and wide field of view. In the conventional FPM model, the sample is assumed to be a 2D thin layer, and a series of low-resolution images at different illumination angles is used for HR image reconstruction. However, in practice, the sample complex amplitude distribution is usually mixed with some unknown background interferences. These background interferences may result from inhomogeneous media distribution or other defocusing layers, etc. The background interference will significantly degrade FPM reconstruction results, but so far it is not considered in the conventional FPM algorithm. Here, we propose a method that adaptively separates background interferences for each illumination angle. Experimental results show that the proposed method has a faster convergence speed and better reconstruction accuracy than the conventional FPM model.