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

Authors:

Pol del Aguila Pla (KTH Royal Institute of Technology)

Keywords:

, image deblurring, image enhancement, inverse problems, nonlinear optimization, scientific imaging