Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging

Due to the limitation of numerical aperture (NA) in a microscope, it is very difficult to obtain a clear image of the specimen with a large depth of field (DOF). We propose a deep learning network model to simultaneously improve the imaging resolution and DOF of optical microscopes. The proposed M-Deblurgan consists of three parts: (i) a deblurring module equipped with an encoder-decoder network for feature extraction, (ii) an optimal approximation module to reduce the error propagation between the two tasks, and (iii) an SR module to super-resolve the image from the output of the optimal approximation module. The experimental results show that the proposed network model reaches the optimal result. The peak signal-to-noise ratio (PSNR) of the method can reach 37.5326, and the structural similarity (SSIM) can reach 0.9551 in the experimental dataset. The method can also be used in other potential applications, such as microscopes, mobile cameras, and telescopes.

Phase-Shifting Projected Fringe Profilometry Using Binary-Encoded Patterns

A phase unwrapping method for phase-shifting projected fringe profilometry is presented. It did not require additional projections to identify the fringe orders. The pattern used for the phase extraction could be used for phase unwrapping directly. By spatially encoding the fringe patterns that were used to perform the phase-shifting technique with binary contrasts, fringe orders could be discerned. For spatially isolated objects or surfaces with large depth discontinuities, unwrapping could be identified without ambiguity. Even though the surface color or reflectivity varied periodically with position, it distinguished the fringe order very well.