SPATIO-TEMPORAL SUPER-RESOLUTION RECONSTRUCTION OF REMOTE SENSING DATA

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 spatio-temporal grid. We perform the super-resolution in the space-time 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.