This paper first defined gray morphology filtering methods based on mathematical morphology, followed with examples of a wood defect image treated before and after open, as well as closed, filtering treatments. By observing the sectional gray distribution of the wood defect image, the effects of the filtering treatments were determined. Results found that gray morphology filtering could effectively remove noise in wood defect images, improve image vision and heighten accuracy for edge detection. The results verified the feasibility of applying gray morphology filtering to treat wood defect images.