为了更好地对印刷电路板光板缺陷图像进行预处理，提出了一种改进的基于全变分模型自适应图像预处理方法。分析了基于全变分范数的图像预处理模型，指出了其存在的缺点。讨论了基于L1 + p 范数的广义的全变分图像预处理模型，分析了其优点与不足之处。提出了一种改进的基于全变分模型自适应图像预处理方法，该方法能尽可能地去除印刷电路板光板缺陷图像中的噪声，同时可克服缺陷图像去噪后存在的边缘模糊与阶梯效应，使去噪后的图像得到增强且具有更加光滑、细腻的视觉效果。对实际获取的印刷电路板光板缺陷图像采用四种图像预处理方法或模型进行了主观与客观实验比较，结果表明，该方法对印刷电路板光板缺陷图像预处理有较好的效果。而且，针对不同的印刷电路板光板缺陷采用该方法都能很好地检测出结果。

In order to actualize image preprocessing of defect image on printed circuit board better, an improved self-adaptive image preprocessing method based on the total variation model is proposed. The image preprocessing model based on the total variation norm is analyzed, and its shortcomings are pointed out. The generalized total variation image preprocessing model based on L1 + p norms is discussed, the advantages and disadvantages are analyzed. An improved self-adaptive image preprocessing method based on the total variation model is proposed, the defect image noise of printed circuit board can be eliminated as far as possible by using the proposed method. At the same time, the edge faintness and ladder effect existed in the defect image can be overcomed better after denoising, and the image after denoising has more slippery and exquisite visual effects. The subjective and objective experimental comparisons among the four image preprocessing methods or models are achieved aiming at the defect image of actual printed circuit board, and the result indicates that the proposed method has good effect on defect image preprocessing of printed circuit board. What is more, the results are all detected better by adopting the proposed method for different printed circuit board defects.