Insulating materials are widely used in fields such as power grids, electrical engineering, and automotive manufacturing. However, defects such as bubbles and inclusions often arise during their processing and service, which not only affect performance but may also pose potential safety hazards. Terahertz technology, with its low radiation and high penetration characteristics, has become a new method for the non-destructive testing of insulating materials. However, its imaging is limited by hardware and optical diffraction constraints, leading to issues such as low resolution, high noise, and low contrast. This study focuses on epoxy resin insulating materials and employs a reflective terahertz time-domain spectroscopy system to perform point-by-point sampling, obtaining waveforms and images for detecting internal defects such as bubbles and inclusions, and analyzing the differences in defect characteristics. To address the issue of inadequate image quality, a preprocessing method combining non-local means filtering and contrast-limited adaptive histogram equalization is proposed to suppress noise and enhance local contrast. Additionally, the SRCNN network is applied for super-resolution reconstruction to improve image details and resolution. Experimental results demonstrate that the proposed method effectively alleviates issues such as low resolution and low contrast in terahertz images, significantly improving defect detection accuracy and reliability.