In order to efficiently remove honeycomb artifacts and restore images in fiber-bundle-based endomicroscopy, we develop a meta-learning algorithm in this work. Two sub-networks are used to extract different levels of features. Meta-training is employed to train the network with small amount of simulated training data, enabling the optimal model to generalize to new tasks not seen in the training set. Numerical results on both USAF target and endomicroscopy images of living mice tissues demonstrate that the algorithm can restore high contrast image without pixilated noise using shorter time. Additionally, no prior information on the shape of the underlying tissues and the distribution of fiber bundles is required, making the method applicable to a variety of fiber-bundle-based endomicroscopy imaging conditions.