Learning-based high-quality image recovery from 1D signals obtained by single-pixel imaging

计算机科学 人工智能 采样(信号处理) 像素 计算机视觉 图像质量 迭代重建 生成对抗网络 压缩传感 过采样 模式识别(心理学)
作者
Xiaogang Wang,Angang Zhu,Shanshan Lin,Bijun Xu
出处
期刊:Optics Communications [Elsevier BV]
卷期号:: 128571-128571
标识
DOI:10.1016/j.optcom.2022.128571
摘要

As an innovative and computational imaging technique, it is critical for single-pixel imaging (SPI) to achieve a high reconstruction quality. However, the reconstruction image quality in conventional SPI is heavily dependent on the sampling rate. In this work, we present a learning-based SPI approach for high-quality image reconstruction under a low sampling rate. A bucket detector included in our optical setup is used to collect the one-dimensional (1D) signals reflected from a two-dimensional (2D) object and an end-to-end generative adversarial network (EGAN) which is pre-trained with simulated data is utilized to implement the reconstruction of the object. The results show that the proposed approach is able to produce high quality approximations of 2D images from optically collected 1D bucket signals at a very low sampling ratio. It can also be shown a better performance can be achieved by compared with previous studies on the same dataset, such as conventional SPI, compressive-sensing ghost imaging (CSGI) and U-net-based SPI approaches. • A learning-based single-pixel imaging (SPI) for high-quality image reconstruction under a very low sampling rate is proposed. • The implementation of efficient image reconstruction is performed by an end-to-end generative adversarial network. • The performance of the proposed SPI is demonstrated under different sampling rate conditions using both simulated and optical experiments. • Compared with other techniques, the pre-trained EGAN can output higher quality approximations of object images.
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