3D deconvolution in Fourier integral microscopy

Fourier integral microscopy (FiMic), also referred to as Fourier light field microscopy (FLFM) in the literature, was recently proposed as an alternative to conventional light field microscopy (LFM). FiMic is designed to overcome the non-uniform lateral resolution limitation specific to LFM. By inserting a micro-lens array at the aperture stop of the microscope objective, the Fourier integral microscope directly captures in a single-shot a series of orthographic views of the scene from different viewpoints. We propose an algorithm for the deconvolution of FiMic data by combining the well known Maximum Likelihood Expectation (MLEM) method with total variation (TV) regularization to cope with noise amplification in conventional Richardson-Lucy deconvolution

What about computational super-resolution in fluorescence Fourier light field microscopy?

Recently, Fourier light field microscopy was proposed to overcome the limitations in conventional light field microscopy by placing a micro-lens array at the aperture stop of the microscope objective instead of the image plane. In this way, a collection of orthographic views from different perspectives are directly captured. When inspecting fluorescent samples, the sensitivity and noise of the sensors are a major concern and large sensor pixels are required to cope with low-light conditions, which implies under-sampling issues. In this context, we analyze the sampling patterns in Fourier light field microscopy to understand to what extent computational super-resolution can be triggered during deconvolution in order to improve the resolution of the 3D reconstruction of the imaged data