Remote Sensing Image Completion Using a Diffusion-Based Propagation Algorithm

In the field of remote sensing it is common to have image data which can be considered in some way to be incomplete. This may relate to missing information caused by sensor failures, cloud cover or partially overlapping data acquisitions. In each of these cases it is of interest to consider how best this data can be completed. Whereas previous work has employed techniques such as low-rank tensor completion to tackle this problem, we present a graph-based propagation algorithm which diffuses entries around the incomplete image tensors. We show this approach is robust in even extreme circumstances for which large regions of image data are missing and compare the quality of our completions against the state of the art. In addition to improved performance as measured by reduced errors versus ground truth in experiments we also provide a comparison of our method's efficiency against benchmark methods and show that the approach is scalable as well as robust.