Design and optimization of lensless phase-retrieval optical system with phase modulation of free-space propagation wavefront is proposed for subpixel imaging to achieve super-resolution reconstruction. Contrary to the traditional super-resolution phase-retrieval, the method in this paper requires a single observation only and uses the advanced Super-Resolution Sparse Phase Amplitude Retrieval (SR-SPAR) iterative technique which contains optimized sparsity based filters and multi-scale filters. The successful object imaging relies on modulation of the object wavefront with a random phase-mask, which generates coded diffracted intensity pattern, allowing us to extract subpixel information. The system’s noise-robustness was investigated and verified. The super-resolution phase-imaging is demonstrated by simulations and physical experiments. The simulations included high quality reconstructions with super-resolution factor of 5, and acceptable at factor up to 9. By physical experiments 3 μ m details were resolved, which are 2.3 times smaller than the resolution following from the Nyquist-Shannon sampling theorem.

We propose a novel approach for lensless single-shot phase retrieval, which provides pixel super-resolution phase imaging. The approach is based on a computational separation of carrying and object wavefronts. The imaging task is to reconstruct the object wavefront, while the carrying wavefront corrects the discrepancies between the computational model and physical elements of an optical system. To reconstruct the carrying wavefront, we do two preliminary tests as system calibration without an object. Essential for phase retrieval noise is suppressed by a combination of sparse- and deep learning-based filters. Robustness to discrepancies in computational models and pixel super-resolution of the proposed approach are shown in simulations and physical experiments. We report an experimental computational super-resolution of 2μm, which is 3.45× smaller than the resolution following from the Nyquist-Shannon sampling theorem for the used camera pixel size of 3.45μm. For phase bio-imaging, we provide Buccal Epithelial Cells reconstructed with a quality close to the quality of a digital holographic system with a 40× magnification objective. Furthermore, the single-shot advantage provides a possibility to record dynamic scenes, where the frame rate is limited only by the used camera. We provide amplitude-phase video clip of a moving alive single-celled eukaryote.

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By the development of artificial Intelligencewhether unintentionallywe are constantly trying to mimic the human senses. Biomimicry, as the starting point, is an engineering approach to emulate nature's well working patterns and strategies. Our goal is to create a standalone artificial system which can respond adequately to various environmental impacts without human intervention. In order to detect these influences over the accuracy of human limitations, the most advanced sensors are needed both in software and hardware. The development in computing power highlights some forgotten algorithms, which were neglected because their complexity made them inefficient on early computers. One of these methods is the Wavelet-Transform Profilometry (WTP) of which successful application is demonstrated in this paper. WTP is a three-dimensional profilometric surface reconstruction algorithm in which orthogonal trajectories are used for high-level signal processing of huge datasets. Our goal was to find a high-precision solution for surface reconstruction by replacing the processing software with advanced mathematical methods rather than use more expensive optical systems.