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- W3207676671 abstract "We present a spatio-temporal super-resolution method for reconstructing a sequence of observations collected by imaging satellites. A sequence of observations is assumed to be defined on a low resolution spatio-temporal grid. It is further assumed that the sequence is generated by blurring of a captured scene with a spatio-temporal convolution kernel and is degraded by noise. Our method simultaneously exhibits deconvolution of the sequence of images from the effects of spatio-temporal blur, denoising of the data, and upsampling of the low-resolution sequence to a high resolution spatiotemporal grid. We perform the super-resolution in the spacetime domain, as opposed to super-resolving the sequence separately and sequentially to a higher spatial and then temporal resolution grid. Simultaneous space-time optimization achieves a more efficient and more accurate reconstruction than reconstructing a sequence frame by frame. The proposed super-resolution methodology is based on total variation regularization and computes the solution using the alternating direction method of multipliers. Numerical results show our approach to be robust and computationally efficient." @default.
- W3207676671 created "2021-10-25" @default.
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- W3207676671 date "2021-07-11" @default.
- W3207676671 modified "2023-10-18" @default.
- W3207676671 title "Spatio-Temporal Super-Resolution Reconstruction of Remote Sensing Data" @default.
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- W3207676671 doi "https://doi.org/10.1109/igarss47720.2021.9553433" @default.
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