Endoir: A GAN-based method for fiber bundle endoscope image restoration

IF 3.5 2区 工程技术 Q2 OPTICS
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引用次数: 0

Abstract

Endoscope plays a crucial role in advancing minimally invasive surgeries. Ultra-compact, agile fiber endoscopes have gained significant popularity as an alternative to traditional bulk imaging systems. They have multiple advantages, such as large field of view, long depth of field and short rigid tip length. However, these systems exhibit honeycomb-like fixed patterns (HFP) and color bias in the output images, which can be attributed to the spacing and cladding around each fiber as well as the physical structure and low light conditions. To address these issues, we propose a fiber endoscope image restoration method based on generative adversarial network (GAN) named Endoir. The generator of Endoir employs a U-Net architecture that incorporates multi-scale skip connections between the encoder and decoder. It can incorporate low-level details with high-level semantics from feature maps in different scales and reduce the number of network parameters to improve the computation efficiency. We generate a synthetic dataset by simulating the fiber endoscope image scheme using an ordinary image dataset as a basis. This approach allows us to obtain a sufficient number of image pairs with more realistic usage scenarios. Our solution not only outperforms previous methods in terms of effectively removing the HFP but also provides the capability to correct color bias. The experiment results show that our method achieves superior accuracy in removing HFP and correcting color bias compared to existing approaches.

Endoir:基于 GAN 的纤维束内窥镜图像修复方法
内窥镜在推进微创手术方面发挥着至关重要的作用。超小型、灵活的光纤内窥镜作为传统大体积成像系统的替代品,已获得广泛欢迎。它们具有多种优势,如视野大、景深长和刚性尖端长度短。然而,这些系统在输出图像中表现出蜂窝状固定图案(HFP)和颜色偏差,这可能是由于每根光纤周围的间距和包层以及物理结构和弱光条件造成的。针对这些问题,我们提出了一种基于生成式对抗网络(GAN)的光纤内窥镜图像修复方法,命名为 Endoir。Endoir 的生成器采用 U-Net 架构,在编码器和解码器之间建立了多尺度跳转连接。它可以从不同尺度的特征图中结合低层次细节和高层次语义,并减少网络参数的数量,从而提高计算效率。我们以普通图像数据集为基础,通过模拟纤维内窥镜图像方案生成合成数据集。通过这种方法,我们可以获得足够数量的图像对,使用场景更加真实。我们的解决方案不仅在有效去除 HFP 方面优于之前的方法,而且还能纠正颜色偏差。实验结果表明,与现有方法相比,我们的方法在去除 HFP 和纠正色彩偏差方面实现了更高的准确性。
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来源期刊
Optics and Lasers in Engineering
Optics and Lasers in Engineering 工程技术-光学
CiteScore
8.90
自引率
8.70%
发文量
384
审稿时长
42 days
期刊介绍: Optics and Lasers in Engineering aims at providing an international forum for the interchange of information on the development of optical techniques and laser technology in engineering. Emphasis is placed on contributions targeted at the practical use of methods and devices, the development and enhancement of solutions and new theoretical concepts for experimental methods. Optics and Lasers in Engineering reflects the main areas in which optical methods are being used and developed for an engineering environment. Manuscripts should offer clear evidence of novelty and significance. Papers focusing on parameter optimization or computational issues are not suitable. Similarly, papers focussed on an application rather than the optical method fall outside the journal''s scope. The scope of the journal is defined to include the following: -Optical Metrology- Optical Methods for 3D visualization and virtual engineering- Optical Techniques for Microsystems- Imaging, Microscopy and Adaptive Optics- Computational Imaging- Laser methods in manufacturing- Integrated optical and photonic sensors- Optics and Photonics in Life Science- Hyperspectral and spectroscopic methods- Infrared and Terahertz techniques
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