Paper
12 January 2018 Pixel-super-resolved lensfree holography using adaptive relaxation factor and positional error correction
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Abstract
Lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and field-of-view (FOV) of conventional lens-based microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). In this paper, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method to address the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Furthermore, an automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target across a wide imaging area of ~29.85 mm2 and achieve half-pitch lateral resolution of 770 nm, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67 μm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jialin Zhang, Qian Chen, Jiasong Sun, Jiaji Li, and Chao Zuo "Pixel-super-resolved lensfree holography using adaptive relaxation factor and positional error correction", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1062004 (12 January 2018); https://doi.org/10.1117/12.2295562
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KEYWORDS
Super resolution

Holograms

Holography

Sensors

Microscopes

Reconstruction algorithms

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