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• Resumen es exacto "In this work we developed a deconvolution method with super-resolution. Making it possible to overcome the resolution limit of an optical system by incorporating the a priori information proposed by the SUPPOSe approach to the deconvolution problem. This approach establishes that it is possible to approximate the real structure as a superposition of point sources of equal intensity, called virtual sources. This allowed us to simplify the deconvolution problem and turn it into an unconstrained minimization problem, where the only unknowns are the positions of the SUPPOSe virtual sources. These positions can take values in the entire domain of the image space. We have tested the SUPPOSe algorithm with two-dimensional images, both in the case of synthetic images and in the case of experimental images acquired with low-resolution systems. Proving that the method can reconstruct the real structure that is distorted in the images. We have created a synthetic data set that simulates different experimental conditions to test the algorithm. From the results obtained we have characterized how the quality of the solution varies depending on the characteristics of the processed image. We also validate these results by processing experimental images with different structures in the sample and under different acquisition conditions."

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