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Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network

arXiv 2107.11574 ↗First posted 24 Jul 2021eess.IV cs.CV physics.opticscited by 0

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Abstract (arXiv)

Reconstruction of image by using convolutional neural networks (CNNs) has been vigorously studied in the last decade. Until now, there have being developed several techniques for imaging of a single object through scattering medium by using neural networks, however how to reconstruct images of more than one object simultaneously seems hard to realize. In this paper, we demonstrate an approach by using generative adversarial network (GAN) to reconstruct images of two adjacent objects through scattering media. We construct an imaging system for imaging of two adjacent objects behind the scattering media. In general, as the light field of two adjacent object images pass through the scattering slab, a speckle pattern is obtained. The designed adversarial network, which is called as YGAN, is employed to reconstruct the images simultaneously. It is shown that based on the trained YGAN, we can reconstruct images of two adjacent objects from one speckle pattern with high fidelity. In addition, we study the influence of the object image types, and the distance between the two adjacent objects on the fidelity of the reconstructed images. Moreover even if another scattering medium is inserted between the two objects, we can also reconstruct the images of two objects from a speckle with high quality. The technique presented in this work can be used for applications in areas of medical image analysis, such as medical image classification, segmentation, and studies of multi-object scattering imaging etc.

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References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
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  8. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  9. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  10. 2016Semantic Segmentation using Adversarial Networkscited by 73
  11. 2018Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guaranteescited by 58
  12. 2018Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Imagescited by 42
3 more
  1. 2019LIP: Local Importance-based Poolingcited by 30
  2. 2018Deep speckle correlation: a deep learning approach towards scalable imaging through scattering mediacited by 23
  3. 2020Deep learning-based virtual refocusing of images using an engineered point-spread functioncited by 6

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