Missing Information Reconstruction for Single Remote Sensing Images Using Structure-Preserving Global Optimization

Filling missing information or removing special objects is often required in the applications of high spatial resolution images. A novel single-image reconstruction method is presented in this letter to solve this task, without the use of any complementary data. First, the spatial pattern of the image is obtained by the statistics of similar patch offsets in the known regions, which provide reliable information for reconstructing the image. The missing regions are then filled by combining a series of shifted pixels via global optimization. The proposed method was tested on a cloudy image for cloud removal and on a public image for military object concealment. The experimental results show that the proposed method can produce visually convincing and coherent reconstructed images, and the accuracy of the reconstruction is better than the existing noncomplementation methods.