Abstract
Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.
Highlights
Single pixel imaging (SPI) [1] illuminates the target scene with structured patterns and records data over time to reconstruct spatial information about a target scene
To observe how image quality deteriorates in Fourier single pixel imaging (FSPI) under-sampled reconstruction, FSPI
This study focused on improving the efficiency of conventional FSPI, which fails to produce
Summary
Single pixel imaging (SPI) [1] illuminates the target scene with structured patterns (random or basis) and records data over time (using a photodetector) to reconstruct spatial information about a target scene. Fourier single pixel imaging (FSPI) is a type of SPI which employs Fourier basis patterns to acquire the Fourier spectrum of a target scene [2]. FSPI achieves better measurement SNR [2] to produce high-quality images. Compared to a basis scan strategy like Hadamard single pixel imaging (HSI), FSPI is known to be more efficient and performs well on under-sampled image reconstruction [7]. By using inverse Fourier transform (IFT), a high-quality target image can be reconstructed. FSPI has gained popularity due to its low-cost design, imaging under background noise, and ability to operate over a long spectral range
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