Objective To effectively extract the defect features on the surface of lithium battery film. Methods Surface de-noising was realized by sparse decomposition algorithm, i. e. , the best atomic function was selected, and sparse decomposition iteration was conducted for defect images with point noise, gaussian noise, salt and pepper noise, as well as additive and multiplicative noise in the over-complete dictionary. The terminating iteration value was got by observation and used as the experience value as the sparse decomposition iteration termination condition for denoising under specific background noise, in order to obtain the denoised defect image. Finally, this method was compared with the median filtering technology. Results Sparse decomposition denoising showed much better performance than the median filter, and had a good recovery for defects in lithium battery film. Conclusion Sparse decomposition algorithm could well remove the noises in lithium battery film image to identify the defects of lithium battery film.