Signal-to-Noise Ratio Improvement for Multiple-Pinhole Imaging Using Supervised Encoder–Decoder Convolutional Neural Network Architecture
Digital image devices have been widely applied in many fields, such as individual recognition and remote sensing. The captured image is a degraded image from the latent observation, where the degradation processing is affected by some factors, such as lighting and noise corruption. Specifically, noi...
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Published in | Photonics Vol. 9; no. 2; p. 69 |
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Format | Journal Article |
Language | English |
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Abstract | Digital image devices have been widely applied in many fields, such as individual recognition and remote sensing. The captured image is a degraded image from the latent observation, where the degradation processing is affected by some factors, such as lighting and noise corruption. Specifically, noise is generated in the processing of transmission and compression from the unknown latent observation. Thus, it is essential to use image denoising techniques to remove noise and recover the latent observation from the given degraded image. In this research, a supervised encoder–decoder convolution neural network was used to fix image distortion stemming from the limited accuracy of inverse filter methods (Wiener filter, Lucy–Richardson deconvolution, etc.). Particularly, we will correct image degradation that mainly stems from duplications arising from multiple-pinhole array imaging. |
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AbstractList | Digital image devices have been widely applied in many fields, such as individual recognition and remote sensing. The captured image is a degraded image from the latent observation, where the degradation processing is affected by some factors, such as lighting and noise corruption. Specifically, noise is generated in the processing of transmission and compression from the unknown latent observation. Thus, it is essential to use image denoising techniques to remove noise and recover the latent observation from the given degraded image. In this research, a supervised encoder–decoder convolution neural network was used to fix image distortion stemming from the limited accuracy of inverse filter methods (Wiener filter, Lucy–Richardson deconvolution, etc.). Particularly, we will correct image degradation that mainly stems from duplications arising from multiple-pinhole array imaging. |
Author | Danan, Eliezer Danan, Yossef Shabairou, Nadav Zalevsky, Zeev |
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Cites_doi | 10.1364/OL.40.001814 10.1364/COSI.2016.CM2B.1 10.1016/S0169-7439(97)00061-0 10.1364/OPTICA.4.001437 10.1038/nature14539 10.3390/s20226551 10.1038/nature03139 10.1145/1275808.1276462 10.3390/s20113013 10.1364/AO.17.003562 10.1016/j.neunet.2014.09.003 10.1109/72.554195 |
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Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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DOI | 10.3390/photonics9020069 |
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SubjectTerms | Aperture Artificial neural networks Cameras coded aperture imaging Coders Compression Computer architecture convolutional neural network Corruption Datasets Deep learning Digital imaging Image degradation Neural networks Object recognition Pinholes Projectors Remote sensing Sensors Signal to noise ratio super-resolution Wiener filtering |
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