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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Bibliographic Details
Published inPhotonics Vol. 9; no. 2; p. 69
Main Authors Danan, Eliezer, Shabairou, Nadav, Danan, Yossef, Zalevsky, Zeev
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
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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Summary: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.
ISSN:2304-6732
2304-6732
DOI:10.3390/photonics9020069