We propose an algorithm for registration between brain tumor images and normal brain images based on tissue recovery. U-Net is first used in BraTS2018 dataset to segment the brain tumors, and PConv-Net is then used to simulate the generation of missing normal tissues in the tumor region to replace the tumor region. Finally, the normal brain image is registered to the tissue recovery brain image. We evaluated the effectiveness of this method by comparing the registration results of the repaired image and the tumor image corresponding to the surrounding tissues of the tumor area. The experimental results showed that the proposed method could reduce the effect of pathological variation, achieve a high registration accuracy, and effectively simulate and generate normal tissues to replace the tumor regions, thus improving the registration effect between brain tumor images and normal brain images.