Abstract
One of the challenges in the utilization of optical satellite images is cloud occlusions. This missing data due to clouds leads to issues in image interpretation and its utilization in different applications. For images, where sparse clouds are present, single image-based restoration is possible; however, cloud dominated images require multi-temporal images for removing clouds. Standard image interpolation-based technique for single image uses only the neighboring pixels information for restoration. The image restoration method based on image inpainting explores the local information of the entire image and propagates the same into the missing gaps. In this paper, the Navier–Stokes-based inpainting algorithm is being used for the restoration. The algorithm propagates the smoothness of the image via partial differential equations(PDEs) and at the same time preserves the edges at the boundary/features of the image. It is analyzed on different satellite images covering different types of terrains and the results show improved statistical characteristics for the restored images.
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Maduskar, D., Dube, N. (2021). Navier–Stokes-Based Image Inpainting for Restoration of Missing Data Due to Clouds. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-15-6067-5_56
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DOI: https://doi.org/10.1007/978-981-15-6067-5_56
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