Application of the Improved Restormer Model for Walnut X-ray Image Denoising

X-ray imaging technology, with its exceptional capability for visualizing internal structures, plays a critical role in the nondestructive testing of agricultural products such as walnuts and almonds. To address the current challenges of high noise levels in walnut X-ray images and the difficulty in preserving critical image details after denoising, this study proposes an improved Restormer denoising model for clearer processing of walnut X-ray images. First, batch normalization layers were introduced into the Multi-Dconv Head Transposed Attention mechanism to enhance the model's understanding of image features. Second, a vertical Total Variation loss function was integrated to suppress high noise levels and extract clearer image features. Experimental results demonstrated that the improved Restormer model achieved a PSNR of 37.30, an SSIM of 0.9358, and an information entropy of 6.5600, representing improvements of 0.16 dB, 0.0002, and 0.0014, respectively, compared to the original Restormer model. In terms of PSNR, SSIM, information entropy, and visual quality, the proposed model outperformed six commonly used network models, including DnCNN and Uformer, delivering clearer images with richer details. Furthermore, on the local dataset, the model also exhibited excellent processing performance, generalization ability, and stability, making it a highly effective solution for walnut X-ray image denoising. The research results can offer a theoretical basis for the efficient image denoising method on walnut X-ray and provide valuable insights for denoising research in other imaging fields.