Existing Liquid Crystal Display (LCD) light-guide plate surface defect detection methods have high missing rate and false detection rate as well as low adaptability to the complex texture structure with gradual change of product surface. Therefore, a LCD light-guide plate surface defect detection method was proposed based on Improved Coherence Enhancing Diffusion (ICED) and Texture Energy Measure-Gaussian Mixture Model (TEM-GMM). Firstly, an ICED model was established, Mean Curvature Flow (MCF) filter was introduced based on the structure tensor, so that the Coherence Enhancing Diffusion (CED) model had better retention effect on the edge of the thin-line defect, and the texture-enhanced and background texture-suppressed filtered image was obtained by using coherence. Then, the texture features of the image were extracted based on the Laws Texture Energy Measure (TEM), and with the texture features of background as the training data for the Gaussian Mixture Model (GMM), the parameters of GMM were estimated by Expectation Maximization (EM) algorithm. Finally, the posterior probability of each pixel in the target image was calculated and used to judge the defect pixel at the online detection stage. The experimental results show that compared with other methods, the missing rate and false detection rate of this method in the distribution of the light-guide particles randomly and the regularly distributed defect image test datasets was 3.27%, 4.32% and 3.59%, 4.87% respectively. The proposed detection method has an extensive application scope, and can effectively detect defects such as scratches, foreign objects, dirt and crushing on the surface of LCD light-guide plate.