Single exposure lensless subpixel phase imaging

Kocsis, Péter; Shevkunov, Igor; Katkovnik, Vladimir; Егиазарян, Карен

doi:10.1117/12.2525679

Cited by 1 publication

(1 citation statement)

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“…The successful super-resolved reconstruction is relying on support constraint on the object plane with known diameter of the laser beam and a sparsity-based block-matching 3D (BM3D) 31 filter. These methods are embedded into a modified iterative Super-Resolution Sparse Phase Amplitude (SR-SPAR) 32 algorithm. The super-resolved reconstructions are demonstrated in simulations and physical experiments, however, the algorithm has difficulty to reconstruct small phase values and provides a generally noisy output.…”

Section: Introductionmentioning

confidence: 99%

SSR-PR: Single-shot Super-Resolution Phase Retrieval based two prior calibration tests

Kocsis,

Shevkunov,

Katkovnik

et al. 2021

Preprint

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We propose a novel approach and algorithm based on two preliminary tests of the optical system elements to enhance the super-resolved complex-valued imaging. The approach is developed for inverse phase imaging in a single-shot lensless optical setup. Imaging is based on wavefront modulation by a single binary phase mask. The preliminary tests compensate errors in the optical system and correct a carrying wavefront, reducing the gap between real-life experiments and computational modeling, which improve imaging significantly both qualitatively and quantitatively. These two tests are performed for observation of the laser beam and phase mask along, and might be considered as a preliminary system calibration. The corrected carrying wavefront is embedded into the proposed iterative Single-shot Super-Resolution Phase Retrieval (SSR-PR) algorithm. Improved initial diffraction pattern upsampling, and a combination of sparse and deep learning based filters achieves the super-resolved reconstructions. Simulations and physical experiments demonstrate the high-quality super-resolution phase imaging. In the simulations, we showed that the SSR-PR algorithm corrects the errors of the proposed optical system and reconstructs phase details 4× smaller than the sensor pixel size. In physical experiment 2 µm thick lines of USAF phase-target were resolved, which is almost 2× smaller than the sensor pixel size and corresponds to the smallest resolvable group of used test target. For phase bio-imaging, we provide Buccal Epithelial Cells reconstructed in computational super-resolution and the quality was of the same level as a digital holographic system with 40× magnification objective. Furthermore, the single-shot advantage provides the possibility to record dynamic scenes, where the framerate is limited only by the used camera. We provide an amplitude-phase video clip of a moving alive single-celled eukaryote.

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Section: Introductionmentioning

confidence: 99%