Restoration of sea surface temperature images by learning-based and optical-flow-based inpainting

Sea surface temperature (SST) images taken from satellites are partially occluded by clouds. In this paper, we propose an inpainting approach for restoration of the partially occluded images. Assuming the sparseness of the SST images, we employ a learning based inpainting for filling the occluded parts. Images taken in the past several days is another clue for filling the occluded parts. These images are regarded as time series data and a video inpainting method is also available. We employ PCA-based inpainting as a learning-based approach and optical-flow-based inpainting as video inpainting, and combine the two restored images according to the expected their restoration error. Experimental results with real satellite images show the effectiveness of our method.