Deblurring and Restoration of Gastroscopy Image Based on Gradient-guidance Generative Adversarial Networks
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摘要: 胃肠镜检查是目前临床上检查和诊断消化道疾病最重要的途径，内窥镜图像的运动模糊会对医生诊断和机器辅助诊断造成干扰。现有的去模糊网络由于缺乏对结构信息的关注，在处理内窥镜图像时普遍存在着伪影和结构变形的问题。为解决这一问题，提高胃镜图像质量，该文提出一种基于梯度指导的生成对抗网络，网络以多尺度残差网络(Res2net)结构作为基础模块，包含图像信息支路和梯度支路两个相互交互的支路，通过梯度支路指导图像去模糊重建，从而更好地保留图像结构信息，消除伪影、缓解结构变形；设计了类轻量化预处理网络来纠正过度模糊，提高训练效率。在传统胃镜和胶囊胃镜数据集上分别进行了实验，实验结果表明，该算法的峰值信噪比(PSNR)和结构相似度(SSIM)指标均优于对比算法，且复原后的视觉效果更佳，无明显伪影和结构变形。Abstract: Gastrointestinal endoscopy plays a critical role in examination and diagnosis upper gastrointestinal diseases. The motion blur of endoscopic images can interfere with doctor's judgment and machine-assisted diagnosis. Due to the lack of attention to structural information in existing deblurring networks, artifacts and structural distortions occur easily when processing endoscopic images. In order to solve this problem and improve the image quality of gastroscopy, a gradient-guided generative adversarial network is proposed in this paper. The network uses the Res2net structure as the backbone, including two interactive branches, the image branch with its intensity and the gradient one. The gradient branch guides the deblurring and reconstruction of the image which in the other branch. Thus more structure information of the image can be kept, with less artifacts and alleviating structural deformation. A quasi-lightweight preprocessing network is designed to correct excessive blur and improve training efficiency. Experiments are performed on the traditional gastroscopy and the capsule gastroscopy datasets. The test results show that the Peak Signal to Noise Ratio(PSNR) and Structural SIMilarity(SSIM) indicators of the algorithm are better than those of the comparison algorithms, and the visual effect of the restored image is evidently improved, without obvious artifacts and structural deformation.
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Key words:
- Gastroscopy image /
- Deblurring /
- Generative adversarial network /
- Gradient guidance
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表 1 不同算法在传统胃镜数据集上的指标测试结果
SRN-DeblurNet 改进SPSR DeblurGAN-v2 本文算法 峰值信噪比(PSNR) 26.40 26.47 26.55 26.89 结构相似度(SSIM) 0.799 0.815 0.818 0.849 
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表 2 不同算法在胶囊胃镜数据集上的指标测试结果
SRN-DeblurNet 改进SPSR DeblurGAN-v2 本文算法 峰值信噪比(PSNR) 迁移 26.93 28.33 28.79 28.83 训练 28.01 28.70 28.81 29.04 结构相似度(SSIM) 迁移 0.777 0.767 0.783 0.801 训练 0.795 0.771 0.813 0.826
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