GAN-SRSPI: super-resolution single-pixel imaging using generative adversarial networks

Zonghao Liu; Huan Zhang; Mi Zhou; Aleksandr Tsoy; Shuming Jiao; Weizhi Wang; Xiao-Ping Zhang; Zihan Geng

doi:10.1117/12.2666860

4 April 2023 GAN-SRSPI: super-resolution single-pixel imaging using generative adversarial networks

Zonghao Liu, Huan Zhang, Mi Zhou, Aleksandr Tsoy, Shuming Jiao, Weizhi Wang, Xiao-Ping Zhang, Zihan Geng

Proceedings Volume 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications; 126177M (2023) https://doi.org/10.1117/12.2666860
Event: 9th Symposium on Novel Photoelectronic Detection Technology and Applications (NDTA 2022), 2022, Hefei, China

Abstract

Single-Pixel Imaging (SPI) techniques enable the reconstruction of an image scene utilizing multiple spatially modulated light patterns and the corresponding measurements from a single-pixel detector. Since in SPI, the acquisition time scales quadratically with the image resolution. A high-resolution image reconstruction suffers from a slow reconstruction speed. This work proposes a super-resolution single-pixel imaging methodology based on generative adversarial networks (GAN-SRSPI). A low-resolution (N×N) image is reconstructed and then super-resolved to obtain a high-resolution (4N× 4N) image. The 4×super-resolution leads to an overall sampling rate of 6.25% (1/16). The previous work only minimizes the mean squared error on the pixel level. The reconstructed image fidelity of the high-frequency part needs to be improved. For the first time, a perceptual loss is proposed in the field of SPI super-resolution. The perceptual loss describes the perceptual similarity instead of pixel-level similarity. An adversarial loss differentiates the original high-resolution image from the super-resolved image. By combining the two, our results are more natural and more consistent with the perceptual characteristics of human eyes. The superiority of the proposed method over the traditional interpolation methods was visually demonstrated in the experiments. Our simulation shows a peak signal-to-noise ratio of 27.65 dB and structural similarity of 0.8076. Our work shows that GAN-SRSPI is a flexible and effective solution for high-resolution and fast SPI.

Citation Download Citation

Zonghao Liu, Huan Zhang, Mi Zhou, Aleksandr Tsoy, Shuming Jiao, Weizhi Wang, Xiao-Ping Zhang, and Zihan Geng "GAN-SRSPI: super-resolution single-pixel imaging using generative adversarial networks", Proc. SPIE 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, 126177M (4 April 2023); https://doi.org/10.1117/12.2666860

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KEYWORDS

Interpolation

Image restoration

Super resolution

Sampling rates

Quantum networks

Imaging systems

Phased array optics

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