Spatio-temporal deconvolution method for enhanced image analysis

Remote sensing observations, whether astronomical or within the solar system, are constrained by instrumental limitations, such as the point spread function in imaging. Ensuring the reliability of scientific analysis from such data requires robust deconvolution techniques. We present a spatio-temporal deconvolution method, to minimise the effect of an extended or complex-shaped point spread function, applicable to dynamic systems with various timescales. This approach enhances observational data by improving image contrast and resolving small-scale dynamic features.

Our method employs a deep neural network trained on state-of-the-art numerical simulations, enabling it to identify dynamic patterns in both spatial and temporal dimensions and to estimate and correct the degradation of intensity contrast. The resulting improvements in intensity representation and resolution facilitate more accurate analyses of small-scale features.

We apply this methodology to solar observations in the millimeter wavelength regime, recovering fine-scale structures critical for understanding the complex behaviour of the solar atmosphere, predict the generation of potentially harmful events, solar flares and the solar wind. By incorporating the temporal domain, our approach surpasses traditional 2D deconvolution techniques.

While initially developed for solar imaging, the method is versatile and can be adapted to various observational contexts across different wavelength regimes. This makes it a valuable tool for advancing future observational studies and expanding research capabilities.