Abstract: In order to enhance the accuracy of X-ray image recognition for weld seam defects, it is essential to employ effective image enhancement techniques. this study investigates investigated the impact of different image enhancement techniques on the quality of weld seam images. The parametersEvaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Structural Clarity, and Information Entropy wereare employed to assess the quality of image enhancement. Experimental results indicated that histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) exhibited superior contrast enhancement effects, while non-local means filtering (NLM) and discrete wavelet transform (DWT) performed well in noise reduction. Image enhancement processing based on CLAHE-NLM proveds to be more effective in assisting deep learning models for weld seam defect classification and recognition. The accuracy and F₁ score of weld seam defect classification reached 97.6% and 96.93%, respectively, representing an improvement of 3.2% and 5.23% compared to the data set without enhancement preprocessing.