This invention discloses a terahertz super-resolution imaging method for defects in terahertz
polyethylene pipe thermofusion joints based on CBAM-ESRGAN, comprising the following steps: Step 1, automatically acquiring detection images using a terahertz
feature parameter imaging method, preprocessing the images to obtain high-resolution and low-resolution images for model training, and dividing them into training and test sets in a 7:3 ratio; Step 2, inputting the
training set from Step 1 into the CBAM-ESRGAN network for training. The beneficial effect of this CBAM-ESRGAN-based terahertz super-resolution imaging method for defects in terahertz
polyethylene pipe thermofusion joints is that it provides an efficient super-resolution
reconstruction method for defects in
polyethylene pipe pores. From terahertz imaging to the construction of a super-resolution
deep learning model, a complete defect detection
system is formed, effectively solving the problems of low acquisition efficiency and blurred edges in terahertz imaging of defects in polyethylene pipe pores.