Abstract: Considering the LCD-Mura defect background reconstructed by current background suppression methods was vulnerable to introduced noise and target defects, a kind of defect image background modeling method based on Singular Value Decomposition (SVD) and maximum entropy was proposed. The singular value sequence was obtained by the SVD of the image pixel matrix. The correspondence between the image components and the singular values was derived by the matrix norm, and the entropy of each component of the image was calculated by the ratio of each component singular value, then effective singular values of background reconstruction was determined by the maximum entropy. Finally, the background was got by the matrix reconstruction, and the general method of evaluating the effect of background reconstruction was put forward. Compared with the three B spline curve fitting methods, the proposed method can improve the contrast of region Mura by 0.59 times at least and the line Mura contrast by 7.71 times at most; and compared with the Discrete Cosine Transform (DCT) method, it reduces the noise of the point Mura by 33.8 percent at least and the line Mura noise by 76.76 percent. The simulation results show that, the model has the advantages of low noise, low loss and high brightness, and can be used to construct the background information of the defect image more accurate.

Key words: Liquid Crystal Display Mura (LCD-Mura) defect, background suppression, Singular Value Decomposition (SVD), maximum entropy, background modeling

摘要： 针对目前液晶显示器斑痕(LCD-Mura)缺陷背景抑制检测中重建的背景存在引入性噪声干扰和目标缺损的问题,提出一种基于奇异值分解(SVD)和最大熵的缺陷图像背景建模方法:通过SVD图像像素矩阵,求得奇异值序列;借助矩阵范式推导出图像分量与奇异值的对应关系,进而以图像各分量奇异值所占比率计算各分量的熵值,以此利用最大熵确定重建背景的有效奇异值;再由矩阵重构得到背景,并进一步提出关于背景重建效果评价的一般方法。相比双三次B样条曲线拟合方法,该方法将区域Mura的对比度最少提升0.59倍,提升线Mura对比度最多达到7.71倍;相比离散余弦变换(DCT)方法,该方法将点Mura的噪声最少降低33.8%,将线Mura噪声降低76.76%。仿真结果表明,该模型具有低噪、低损和高亮的优点,能够更为准确地构建出缺陷图像的背景信息。

关键词: 液晶显示器斑痕缺陷, 背景抑制, 奇异值分解, 最大熵, 背景建模