Wavefront randomization improves deconvolution

Amit Kohli; Anastasios N. Angelopoulos; Laura Waller

doi:10.1117/12.3003742

13 March 2024 Wavefront randomization improves deconvolution

Amit Kohli, Anastasios N. Angelopoulos, Laura Waller

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 1285708 (2024) https://doi.org/10.1117/12.3003742
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image. Digital correction techniques, such as deconvolution, have limited ability to correct the blur, since some spatial frequencies in the scene are not measured adequately due to the aberrations (‘zeros’ of the system transfer function). Our work proves that the addition of a random mask to an imaging system removes its dependence on aberrations, resulting in no systematic zeros in the transfer function and consequently less sensitivity to noise during deconvolution. In simulation, we show that this strategy improves image quality over a range of aberration types, aberration strengths, and signal-to-noise ratios.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Amit Kohli, Anastasios N. Angelopoulos, and Laura Waller "Wavefront randomization improves deconvolution", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 1285708 (13 March 2024); https://doi.org/10.1117/12.3003742

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