During the harvesting and storage of wheat, various impurities are often mixed in, which adversely affect the processing quality and food safety of wheat. Therefore, developing an efficient and accurate impurity detection method is of great importance. Terahertz (THz) imaging technology can acquire time-domain spectral transmission images of wheat impurities, providing more features and facilitating detection. However, due to the limitations of THz imaging system hardware and environmental factors, the acquired THz images are often contaminated with noise, resulting in blurred details and indistinct edges, which severely hinder the accurate identification of impurities. To improve the quality of THz images of wheat impurities, this study proposes an Edge-Enhanced and Feature-Fused Image Denoising Network (EEFDNet). The proposed network employs a dual-branch architecture: a denoising branch utilizing dilated convolutions to strengthen feature representation, and an edge enhancement branch designed to emphasize impurity contour information. The outputs of the two branches are integrated through a feature fusion module to effectively remove noise while preserving and enhancing structural details. Experimental results on a self-established THz image dataset of wheat impurities demonstrate that EEFDNet exhibits superior performance, with the PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) reaching 32.59 dB and 0.9180, respectively, outperforming several mainstream denoising models. Moreover, the proposed method exhibits strong robustness under high-noise conditions. This study provides an effective image preprocessing approach for wheat impurity detection and establishes a solid foundation for subsequent high-precision impurity identification.
Jiang et al. (Sat,) studied this question.