Signal-to-Noise Ratio Improvement for Multiple-Pinhole Imaging Using Supervised Encoder–Decoder Convolutional Neural Network Architecture

• Published in: Photonics Vol. 9; no. 2; p. 69
• Main Authors: Danan, Eliezer, Shabairou, Nadav, Danan, Yossef, Zalevsky, Zeev
• Format: Journal Article
• Language: English
• 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
• DOI: 10.3390/photonics9020069
• ISSN: 2304-6732

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.