Temporal Super-Resolution of Microwave Remote Sensing Images

We develop an approach for increasing the temporal resolution of a temporally blurred sequence of observations. Super-resolution is performed in time using a variational approach. By temporal super-resolution, we mean recovering rapidly evolving events that were corrupted by the induced blur of the sensor. A blurred sequence of observations is assumed to have been generated by convolution of a physical scene with a temporal rectangular convolution kernel whose support is the sensor exposure time. We solve the deconvolution problem using the Split-Bregman method. Such methodology is based on current research in sparse optimization and compressed sensing, which lead to unprecedented efficiencies for solving image reconstruction problems. We test our method using a simulated temporally blurred and noisy temporal precipitation sequence and show that our method significantly reduces the errors in the corrupted sequence.