This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challen...This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challenges in efficient decoding,we propose a method that optimizes output processing through scalar multiplications,enhancing performance in generating higher-dimensional outputs.By employing on-system iterative tuning,we mitigate hardware imperfections and noise,progressively improving image reconstruction accuracy to near-digital quality.Furthermore,our approach supports noise reduction and optical image generation,enabling models such as denoising autoencoders,variational autoencoders,and generative adversarial networks.Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems,enabling real-time,low-power image processing in applications such as medical imaging,autonomous vehicles,and edge computing.展开更多
基金supported by the National Research Foundation(NRF)of Korea funded by the Ministry of Science,ICT and Future Planning(MSIP)of Korea(Nos.RS-2021-NR060087 and RS-2024-00353762)。
文摘This study introduces an optical neural network(ONN)-based autoencoder for efficient image processing,utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks.To address the challenges in efficient decoding,we propose a method that optimizes output processing through scalar multiplications,enhancing performance in generating higher-dimensional outputs.By employing on-system iterative tuning,we mitigate hardware imperfections and noise,progressively improving image reconstruction accuracy to near-digital quality.Furthermore,our approach supports noise reduction and optical image generation,enabling models such as denoising autoencoders,variational autoencoders,and generative adversarial networks.Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems,enabling real-time,low-power image processing in applications such as medical imaging,autonomous vehicles,and edge computing.
