Open AccessDQN based single-pixel imaging
Author(s) -
Zhirun Wang,
Wenjing Zhao,
Aiping Zhai,
Peng He,
Dong Wang
Publication year - 2021
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.422636
Subject(s) - sampling (signal processing) , computer science , pixel , path (computing) , artificial intelligence , image quality , process (computing) , oversampling , algorithm , computer vision , image (mathematics) , telecommunications , bandwidth (computing) , filter (signal processing) , programming language , operating system
For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments. Thanks to the DQN, the proposed single-pixel imaging technique is capable of obtaining an optimal sampling strategy directly, and therefore it requires no artificial planning of the sampling path there, which eliminates the influence of the imperfect sampling path planning on the imaging performance.