Deep Learning-Based Image Denoising Approach for the Identification of Structured Light Modes in Dusty Weather
Structured light is gaining importance in free-space communication. Classifying spatially-structured light modes is challenging in a dusty environment because of the distortion on the propagating beams. This article addresses this challenge by proposing a deep learning convolutional autoencoder algo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10223284/ |
| Summary: | Structured light is gaining importance in free-space communication. Classifying spatially-structured light modes is challenging in a dusty environment because of the distortion on the propagating beams. This article addresses this challenge by proposing a deep learning convolutional autoencoder algorithm for modes denoising followed by a neural network for modes classification. The input to the classifier was set to be either the denoised image or the latent code of the convolutional autoencoder. This code is a low-dimensional representation of the inputted images. The proposed machine learning (ML) models were trained and tested using laboratory-generated mode data sets from the Laguerre and Hermite Gaussian mode bases. The results show that the two proposed approaches achieve an average classification accuracy exceeding 98%, and both are better than the classification accuracy reported recently (83–91%) in the literature. |
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| ISSN: | 1943-0655 |