Noise Reduction Method for Intestinal Image Acquired by Intestinal Robot

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.

Keywords

• wireless powered intestinal robot
• non-subsampled contourlet transform
• convolutional neural network (cnn)