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Disparity-constrained stereo endoscopic image super-resolution

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

With the increasing usage of stereo cameras in computer-assisted surgery techniques, surgeons can benefit from better 3D context of the surgical site in minimally invasive operations. However, since stereo cameras are placed together at the confined endoscope tip, the size of lens and sensors is limited, resulting in low resolution of stereo endoscopic images. How to effectively exploit and utilize stereo information in stereo endoscopic super-resolution (SR) becomes a challenging problem.

Methods

In this work, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to reconstruct images using a stereo image pair. In particular, a disparity constraint mechanism is incorporated into the generation of SR images in the deep neural network framework with effective feature extractors and atrous parallax attention modules.

Results

Extensive experiments were conducted to evaluate the performance of proposed DCSSRnet on the da Vinci dataset and Medtronic dataset. The results on endoscopic image datasets demonstrate that the proposed approach produces a more effective improvement over current SR methods on both quantitative measurements. The ablation studies further verify the effectiveness of the components of the proposed framework.

Conclusion

The proposed DCSSRnet provides a promising solution on enhancing the spatial resolution of stereo endoscopic image pairs. Specifically, the disparity consistency of the stereo image pair provides informative supervision for image reconstruction. The proposed model can serve as a tool for improving the quality of stereo endoscopic images of endoscopic surgery systems.

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Acknowledgements

The authors would like to acknowledge Medtronic Inc. for providing part of stereo image data.

Funding

This research was partly supported by National Key R&D Program of China (No. 2019YFB1311503), National Natural Science Foundation of China (No. 62003208), Committee of Science and Technology, Shanghai, China (No. 19510711200), and Shanghai Sailing Program (No. 20YF1420800).

Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

    Tianyi Zhang, Yun Gu, Xiaolin Huang & Jie Yang

  2. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

    Tianyi Zhang, Yun Gu, Xiaolin Huang, Jie Yang & Guang-Zhong Yang

  3. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Guang-Zhong Yang

Authors
  1. Tianyi Zhang
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  2. Yun Gu
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  3. Xiaolin Huang
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  4. Jie Yang
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  5. Guang-Zhong Yang
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Corresponding author

Correspondence to Jie Yang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

The study conducted experiments based on open-source available datasets. For this type of study, formal consent is not required.

Code availability

The code we used is available at https://github.com/hgfe/DCSSR.

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Zhang, T., Gu, Y., Huang, X. et al. Disparity-constrained stereo endoscopic image super-resolution. Int J CARS 17, 867–875 (2022). https://doi.org/10.1007/s11548-022-02611-5

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  • DOI: https://doi.org/10.1007/s11548-022-02611-5

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