Based on the sand-mud interlayer core of a block in Ordos Basin, denoising neural network based on wavelet transformation (DWTNet) was used to denoise the core image. The evaluation of this method was carried out by comparing the peak signal-tonoise ratio (PSNR) and the post-denoising image outcomes. The investigation reveals that by applying the DWTNet denoising algorithm to the test sets YX1 and YX2, and contrasting it with other denoising algorithms such as EGDNet, the PSNR values at noise levels of 25, 50, and 75 dB are respectively 0.527, 0.418, and 1.1 dB higher than those achieved by the EGDNet algorithm. The proposed algorithm surpasses others in terms of metrics including peak signal to noise ratio(PSNR), and visually, the resulting images processed by it exhibit enhanced clarity. The introduction of this method holds substantial significance for the calculation of parameters like porosity, mean specific surface area, mean curvature, among other rock properties, thereby advancing the capabilities in digital core technology, CT scanning analysis, and understanding of rock characteristics.