A pipeline robot detection image enhancement method based on image fusion and improved threshold

Pipeline robot detection technology can quickly, accurately and intuitively identify the structure and hidden functional troubles of pipeline. However, due to the restriction of the pipeline environment, the detected images have problems such as uneven illumination, low contrast and blurred details. Therefore, an enhancement technique for detected image of pipeline robot is proposed. First, the contrast limited adaptive histogram equalization(CLAHE) and homomorphic filtrate(HF) are applied to deal with the problem of uneven illumination and low contrast, and the result images of the two methods are fused. Secondly, the fusion images are transformed by the Nonsubsampled Contourlet Transform(NSCT), and the improved Bayes-Shrink threshold is used to remove the noise of the high frequency coefficient. Finally, the nonlinear mapping function is used to enhance the details, and the NSCT inverse transform is used to get the final enhanced image. In order to verify the effectiveness and superiority of the method for pipeline robot detection image, 5 typical pipeline robot detection images were selected and enhanced by this method, and compared with 4 common image enhancement technologies. The results show that image enhancement method for pipeline robot detection image by using image fusion and improved threshold can effectively improve the overall and local contrast image, and effectively enhance the image details. It can solve the main problems in pipeline robot detected image effectively.