Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) investigation of nanoscale structures in biological samples, offering unique insights into their organization. However, traditional 3D super-resolution microscopy using high numerical aperture (NA) objectives is limited by imaging depth of field (DOF), restricting their practical application to relatively thin biological samples. Here, we developed a unified solution for thick sample super-resolution imaging using a deformable mirror (DM) which served for fast remote focusing, optimized point spread function (PSF) engineering, and accurate aberration correction. By effectively correcting the system aberrations introduced during remote focusing and sample aberrations at different imaging depths, we achieved high-accuracy, large DOF imaging (∼8μm) of the whole-cell organelles [i.e., nuclear pore complex (NPC), microtubules, and mitochondria] with a nearly uniform resolution of approximately 35 nm across the entire cellular volume.

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution. In this work, an all-optical diffractive neural network (DPENet) based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed. Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges. Simulation results show that DPENet achieves F-scores of 0.9308 (MNIST) and 0.9352 (NIST) and enables real-time edge detection of biological cells, achieving an F-score of 0.7462.