Fiber bundle image restoration using conditional generative adversarial network

Baoteng Xu; Jialin Liu; Wei Zhou; Daxi Xiong; Xibin Yang

doi:10.1117/12.2579861

5 November 2020 Fiber bundle image restoration using conditional generative adversarial network

Baoteng Xu, Jialin Liu, Wei Zhou, Daxi Xiong, Xibin Yang

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650X (2020) https://doi.org/10.1117/12.2579861
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Fiber bundle have been widely used in biomedical endoscopy areas because of its flexibility and high spatial resolution. However, due to the irregular layout of the core and surrounding cladding, the captured fiber bundle images are affected by repeated honeycomb-like patterns. In this paper, we investigate the conditional generative adversarial network to reconstruct the optical fiber endoscope image. In order to improve the operation efficiency and reduce the burden of generative network, we use traditional method to obtain the fiber location map, as an additional condition to generate the network together with the input image. By injecting the position information of fibers, the generative network will pay more attention to the fiber regions and the surrounding structures, and the discriminative network will be able to assess the local consistency of the restored regions. In order to obtain more extensive contextual information, we apply multi-scale loss on the decoder side of the autoencoder. Each of these losses compares the difference between the output of the convolutional layer and the ground truth value that has been downscaled accordingly. Aside from the multi-scale losses, which are based on a pixel-by-pixel operation, we also add a perceptual loss that measures the global discrepancy between the features of the autoencoder’s output and those of the corresponding ground-truth clean image. Our experiments show the effectiveness of our approach which can effectively remove the honeycomb-like patterns and retain the original image features.

Citation Download Citation

Baoteng Xu, Jialin Liu, Wei Zhou, Daxi Xiong, and Xibin Yang "Fiber bundle image restoration using conditional generative adversarial network", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650X (5 November 2020); https://doi.org/10.1117/12.2579861

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KEYWORDS

Image restoration

Structured optical fibers

Image quality

Optical fibers

Endoscopes

Spatial resolution

Imaging systems

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