Application and influencing factors analysis of Pix2pix network in scattering imaging

Hu, Yongqiang; Zhong, Tang; Hu, J. F.; Lu, Xuehua; Zhang, Wenpeng; Xie, Zhenghui; Zuo, Haoyi; Li, Ling; Huang, Yijia

doi:10.1016/j.optcom.2023.129488

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…In the field of computational imaging, emerging technologies represented by computer vision and deep learning have been constantly emerging. Many studies have shown that deep learning can overcome the limitations of traditional scattering imaging methods, providing a more persistent and stable imaging technique [22][23][24][25]. Sinha et al [26] introduced convolutional neural network (CNN) into lensless imaging through scattering media by training on the complex dataset ImageNet.…”

Section: Introductionmentioning

confidence: 99%

Study on transfer learning-based cross-spectral speckle image reconstruction method

Zhao,

Zhang,

et al. 2024

Phys. Scr.

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In recent years, convolutional neural networks (CNNs) have been successfully applied to reconstruct images from speckle patterns generated as objects pass through scattering media. To achieve this objective, a large amount of data is collected for training the CNN. However, in certain cases, the characteristics of light passing through the scattering medium may vary. In such situations, it is necessary to collect a substantial amount of new data to re-train the CNN and achieve image reconstruction. To address this challenge, transfer learning techniques are introduced in this study. Specifically, we propose a novel Residual U-Net Generative Adversarial Network, denoted as ResU-GAN. The network is initially pre-trained using a large amount of data collected from either visible or non-visible light sources, and subsequently fine-tuned using a small amount of data collected from non-visible or visible light sources. Experimental results demonstrate the outstanding reconstruction performance of the ResU-GAN network. Furthermore, by combining transfer learning techniques, the network enables the reconstruction of speckle images across different datasets. The findings presented in this paper provide a more generalized approach for utilizing CNNs in cross-spectral speckle imaging.

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