Abstract: Abstract: Because of the influence of the environment and the characteristics of the locust slices, locust slices obtained from experiments are always incomplete or folded. To match and reconstruct this folded area, the slice in the sequences adjacent to the slice which needs to be repaired is used as the reference image, and in this way, the slice is repaired successfully. The image mosaic and repair algorithm used in the article has the following steps: image preprocessing, feature extraction, feature matching, coordinate transformation and image mosaic and repair. Firstly, as there is some noise in the slice image that can influence the repairing effect, we use wavelet denoising on images to reduce the effect of noise on feature extraction before searching the feature points; secondly, as there are no related materials about how to match or repair locust slices, in order to find more suitable feature extraction methods for locust slices, we compare the scale-invariant feature transform (SIFT) with speed-up robust features (SURF) algorithm which are already widely used in computer vision and image registration, and then generate the image feature vectors to complete the feature extraction from the initial slice image, record the scale, position and direction of the feature points at the same time, and finish the preliminary matching between the two slices; thirdly, as the preliminary matching can not completely remove the error matching points, random sample consensus (RANSAC) algorithm is used to correct the matching errors and eliminate the error matching points by repeatedly choosing a group of random subsets of the data, finally leaving the correct matching points; fourthly, the homography matrix is calculated by using the least square method based on all the correct matching points, which makes full use of existing points to calculate and therefore makes the results have more universal adaptability; finally, the locust slices mosaic is finished using the correct matching points and the homography matrix, the corresponding matching block for the folded part is found out using space mapping model and the slice repair is completed. The experiment adopts two groups of positive images of locust slice obtained from the experimental environment under the microscope. What's more, statistical results of contrast experiment with SIFT and SURF algorithm are obtained, each of which shows the running time, the number of matching points and matching rate. The experimental results show that: the matching rates reach 72.2% and 34.0% using SIFT algorithm and SURF algorithm, respectively, which means that SIFT algorithm can meet the requirements of image mosaic more precisely compared with SURF algorithm. As the matching rate doesn't perform as well as expected, the error sources such as the change of content or the effect from the fold are briefly analyzed. The registration algorithm provides the reference for the later work of the folded area's opening on the slice image and the three-dimensional reconstruction of locust.