Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks

How to cite: Jayavel, A.; Gopinath, S.; A, P.P.; Arockiaraj, F.G.; Bleahu, A.; Xavier, A.P.I.; Slobozhan, I.; Han, M.; Smith, D.; Ng, S.H.; Katkus, T.; John Francis Rajeswary, A.S.; Sharma, R.; Juodkazis, S.; Anand, V. Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks. Preprints 2023, 2023020408. https://doi.org/10.20944/preprints202302.0408.v1. Jayavel, A.; Gopinath, S.; A, P.P.; Arockiaraj, F.G.; Bleahu, A.; Xavier, A.P.I.; Slobozhan, I.; Han, M.; Smith, D.; Ng, S.H.; Katkus, T.; John Francis Rajeswary, A.S.; Sharma, R.; Juodkazis, S.; Anand, V. Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks. Preprints 2023, 2023020408. https://doi.org/10.20944/preprints202302.0408.v1.

Cite as:

Jayavel, A.; Gopinath, S.; A, P.P.; Arockiaraj, F.G.; Bleahu, A.; Xavier, A.P.I.; Slobozhan, I.; Han, M.; Smith, D.; Ng, S.H.; Katkus, T.; John Francis Rajeswary, A.S.; Sharma, R.; Juodkazis, S.; Anand, V. Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks. Preprints 2023, 2023020408. https://doi.org/10.20944/preprints202302.0408.v1. Jayavel, A.; Gopinath, S.; A, P.P.; Arockiaraj, F.G.; Bleahu, A.; Xavier, A.P.I.; Slobozhan, I.; Han, M.; Smith, D.; Ng, S.H.; Katkus, T.; John Francis Rajeswary, A.S.; Sharma, R.; Juodkazis, S.; Anand, V. Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks. Preprints 2023, 2023020408. https://doi.org/10.20944/preprints202302.0408.v1.

Abstract

Pattern recognition techniques form the heart of most, if not all, incoherent linear shift-invariant systems. When an object is recorded using a camera, the object information gets sampled by the point spread function (PSF) of the system, replacing every object point with the PSF in the sensor. The PSF is a sharp Kronecker Delta-like function when the numerical aperture (NA) is large with no aberrations. When the NA is small, and the system has aberrations, the PSF appears blurred. In the above case, if the PSF is known, then the object information can be obtained by scanning the PSF over the recorded object intensity pattern and looking for pattern matching conditions through a mathematical process called correlation. In this study, a recently developed deconvolution method, the Lucy-Richardson-Rosen algorithm (LR2A), has been implemented to computationally refocus images recorded in the presence of spatio-spectral aberrations. The performance of LR2A was compared against the Lucy-Richardson algorithm and non-linear reconstruction. LR2A exhibits a superior deconvolution capability even in extreme cases of spatio-spectral aberrations and blur. Experimental results of deblurring a picture captured using high-resolution smartphone cameras are presented. LR2A was implemented to significantly improve the performances of the widely used deep convolutional neural networks for image classification.

Keywords

imaging; deblurring; deep learning; image classification; Lucy-Richardson algorithm; holography; aberrations; diffraction; incoherent optics; smart phone

Subject

PHYSICAL SCIENCES, Optics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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