When large areas of an image are missing owing to unspecified factors, existing image restoration models usually cannot repair the image effectively, leading to repair results that suffer from discontinuity in their characteristics. This study proposes a fine restoration method for incomplete images with external and image features. First, we improved the dynamic memory networks (DMN+) in our study. DMN+ scheme combines the in-field features of an incomplete image and the related off-field features, generating an optimized image of the defective image containing external and image features. Next, a generative adversarial generative network with piecewise gradient penalty constraints is constructed. The network instructs the generator to perform a coarse repair on the optimized mutilated image, which results in a coarse repair image of the target to be repaired. Finally, the coarse restoration map is further optimized based on the idea of coherence of related features, and a final fine restoration image is obtained. The algorithm proposed here is verified on three image data sets with varying complexities. Moreover, the visual effects and objective data results of the proposed algorithm are compared with those of the existing dominant restoration model. The restoration results of our model are more structurally sound in terms of texture. Furthermore, our model is superior to other models in terms of both visual effects and objective data. The peak signal-to-noise ratio in the most challenging Underwater Targe dataset is 27.01, with a structural similarity index of 0.949.