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Article

Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm

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Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
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PG & Research Department of Physics, The American College, Madurai 625002, India
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PG & Research Department of Physics, Thiagarajar College, Madurai 625009, India
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Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
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Laboratory of Nonlinear Optics, University of Latvia, LV-1004 Riga, Latvia
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Hee Photonic Labs, LV-1002 Riga, Latvia
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Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent 100000, Uzbekistan
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Tokyo Tech World Research Hub Initiative (WRHI), School of Materials and Chemical Technology, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
*
Author to whom correspondence should be addressed.
Photonics 2022, 9(9), 625; https://doi.org/10.3390/photonics9090625
Received: 29 July 2022 / Revised: 25 August 2022 / Accepted: 29 August 2022 / Published: 31 August 2022
(This article belongs to the Special Issue Advances and Application of Imaging on Digital Holography)
A refractive lens is one of the simplest, most cost-effective and easily available imaging elements. Given a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can only be obtained by changing either the location of the lens, object or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The imaging process consists of three steps. In the first step, point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions during the final step. The results of LRRA were compared with two well-known reconstruction methods, namely the Lucy-Richardson algorithm and non-linear reconstruction. View Full-Text
Keywords: imaging; incoherent optics; Lucy-Richardson-Rosen algorithm; deblurring; refractive lens; computational imaging; holography; 3D imaging; deconvolution imaging; incoherent optics; Lucy-Richardson-Rosen algorithm; deblurring; refractive lens; computational imaging; holography; 3D imaging; deconvolution
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MDPI and ACS Style

Praveen, P.A.; Arockiaraj, F.G.; Gopinath, S.; Smith, D.; Kahro, T.; Valdma, S.-M.; Bleahu, A.; Ng, S.H.; Reddy, A.N.K.; Katkus, T.; Rajeswary, A.S.J.F.; Ganeev, R.A.; Pikker, S.; Kukli, K.; Tamm, A.; Juodkazis, S.; Anand, V. Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm. Photonics 2022, 9, 625. https://doi.org/10.3390/photonics9090625

AMA Style

Praveen PA, Arockiaraj FG, Gopinath S, Smith D, Kahro T, Valdma S-M, Bleahu A, Ng SH, Reddy ANK, Katkus T, Rajeswary ASJF, Ganeev RA, Pikker S, Kukli K, Tamm A, Juodkazis S, Anand V. Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm. Photonics. 2022; 9(9):625. https://doi.org/10.3390/photonics9090625

Chicago/Turabian Style

Praveen, P. A., Francis Gracy Arockiaraj, Shivasubramanian Gopinath, Daniel Smith, Tauno Kahro, Sandhra-Mirella Valdma, Andrei Bleahu, Soon Hock Ng, Andra Naresh Kumar Reddy, Tomas Katkus, Aravind Simon John Francis Rajeswary, Rashid A. Ganeev, Siim Pikker, Kaupo Kukli, Aile Tamm, Saulius Juodkazis, and Vijayakumar Anand. 2022. "Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm" Photonics 9, no. 9: 625. https://doi.org/10.3390/photonics9090625

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