上海交通大学学报

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (10): 1303-1309.doi: 10.16183/j.cnki.jsjtu.2020.245

所属专题： 《上海交通大学学报》2021年12期专题汇总专辑； 《上海交通大学学报》2021年“自动化技术、计算机技术”专题

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肠道机器人获取的肠道图像降噪处理方法

薛蓉蓉, 王志武(), 颜国正, 庄浩宇   

  1. 上海交通大学 医疗机器人研究院, 上海 200240
  • 收稿日期:2020-07-30 出版日期:2021-10-28 发布日期:2021-11-01
  • 通讯作者: 王志武 E-mail:zwwang@sjtu.edu.cn
  • 作者简介:薛蓉蓉(1995-),女,江苏省东台市人,硕士生,主要研究方向为图像处理.
  • 基金资助:
    国家自然科学基金(61673271);国家自然科学基金(81971767);上海市科研项目(19441910600);上海市科研项目(19441913800);上海市科研项目(19142203800);上海交通大学医疗机器人研究院项目(IMR2018KY05)

Noise Reduction Method for Intestinal Image Acquired by Intestinal Robot

XUE Rongrong, WANG Zhiwu(), YAN Guozheng, ZHUANG Haoyu   

  1. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2020-07-30 Online:2021-10-28 Published:2021-11-01
  • Contact: WANG Zhiwu E-mail:zwwang@sjtu.edu.cn

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摘要：

无线供能式肠道机器人通过图像采集系统将拍摄到的肠道图像传输到体外上位机供医生诊断,但由于图像传输过程会受到电路结构、外在环境等干扰,导致采集到的图像中出现噪声.为此,提出一种基于非下采样轮廓变换(NSCT)的无线供能式肠道机器人采集图片的降噪算法.首先,利用直方图均衡化预处理,提升肠道噪声图片的亮度和对比度;其次,对肠道噪声图片进行NSCT变换并构建残差网络模型对变换后的频率域信息降噪;最后,利用NSCT反变换重构得到降噪后的图像.结果表明:该算法能够有效地降低复杂环境中肠道图片受噪声的影响,较好地保持图像的视觉效果.与其他智能算法模型相比,主客观降噪效果均有所提高,峰值信噪比(PSNR)提升了1.35~3.45 dB,结构相似性(SSIM)提高了 0.0083~0.0252.

关键词: 无线供能式肠道机器人, 非下采样轮廓变换, 卷积神经网络

Abstract:

The wireless powered intestinal robot transmits the intestinal images taken by the image acquisition system to the external upper computer for diagnosis. However, the image transmission process will be interfered by the circuit structure and external environment, leading to noise in the collected images. Therefore, an image denoising algorithm based on non-subsampled contourlet transform (NSCT) is proposed to reduce the noise of the images collected by intestinal robots. First, histogram equalization pretreatment is adopted to improve the brightness and contrast of intestinal noise images. Next, NSCT transformation is performed on intestinal noise images and a residual network model is constructed to reduce the noise of frequency domain information after transformation. Finally, the denoised image is reconstructed by NSCT inverse transformation. The results show that the proposed algorithm can effectively reduce the influence of intestinal noise in complex environments, and better maintain the visual effect of the image. Compared with other intelligent algorithm models, both subjective and objective noise reduction effects are improved, with peak signal to noise ratio (PSNR) improved by 1.35 to 3.45 dB and structural similarity index measure (SSIM) improved by 0.0083 to 0.0252.

Key words: wireless powered intestinal robot, non-subsampled contourlet transform, convolutional neural network (CNN)

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