SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network

• URL: http://arxiv.org/abs/2107.01330v1
• Date: Sat, 3 Jul 2021 03:06:09 GMT
• Title: SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network
• Authors: Nazmul Karim and Nazanin Rahnavard
• Abstract summary: We propose a generative adversarial network-based reconstruction framework for single-pixel imaging, referred to as SPI-GAN. Our method can reconstruct images with 17.92 dB PSNR and 0.487 SSIM, even if the sampling ratio drops to 5%.
• Score: 6.722629246312285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Single-pixel imaging is a novel imaging scheme that has gained popularity due to its huge computational gain and potential for a low-cost alternative to imaging beyond the visible spectrum. The traditional reconstruction methods struggle to produce a clear recovery when one limits the number of illumination patterns from a spatial light modulator. As a remedy, several deep-learning-based solutions have been proposed which lack good generalization ability due to the architectural setup and loss functions. In this paper, we propose a generative adversarial network-based reconstruction framework for single-pixel imaging, referred to as SPI-GAN. Our method can reconstruct images with 17.92 dB PSNR and 0.487 SSIM, even if the sampling ratio drops to 5%. This facilitates much faster reconstruction making our method suitable for single-pixel video. Furthermore, our ResNet-like architecture for the generator leads to useful representation learning that allows us to reconstruct completely unseen objects. The experimental results demonstrate that SPI-GAN achieves significant performance gain, e.g. near 3dB PSNR gain, over the current state-of-the-art method.

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