基于梯度指导的生成对抗网络内镜图像去模糊重建

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...

Deblurring and Restoration of Gastroscopy Image Based on Gradient-guidance Generative Adversarial Networks

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.