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In this poster, I will present an inverse problem framework to detect point and extended sources in scientific imaging. With clear applications in biology, medical sciences and astronomy, among others, our proposal is a blind deconvolution approach that allows for spatially variant point spread functions. The methodology builds on the extensive research in convolutional coding in deep learning and signal processing, and introduces group-sparsity constraints to this context.
This is poster number 66 in Poster Session