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Heterogeneous multidimensional data deblurring

We present a new scheme for deconvolution of heterogeneous multidimensional data (e.g. spatio-temporal or spatio-spectral). It is derived, in a very general way, following an inverse problem approach. This method exploits the continuity of both object and PSF along the different dimensions to elaborate separable constraints. This improves the effectiveness and the robustness of the deconvolution technique. We demonstrate these improvements by processing real X-ray video sequences (x, y, t) and astronomical multi-spectral images (x, y, λ).