lens and the source profile with analytical models (such as the Singular Isothermal Ellipsoid or the Sersic profile), and they explore the parameter space to obtain the set that minimizes a $\chi^2$ statistics between the observed and modeled surface brightness counts distribution on the lens plane. Whenever the source morphology is complex or clumpy (such as in case of dusty star-forming galaxies) the results tend to be a oversemplification of the real physical situation. Non parametric’ or ’semi-parametric’ methods aim to reconstruct the original source morphology by assuming a pixellated distribution for the surface brightness of the background galaxy. They exploits a simple matrix formalism with the introduction of a regularisation term that ensures a certain degree of smoothness in the reconstructed source (Warren and Dye 2003; Suyu et al. 2006)

In this work, we adopt the Regularized Semilinear Inversion method of Warren and Dye (2003), improved with the adoption of an adaptive source plane pixelization scheme (Nightingale and Dye 2015), and extended to deal with interferometric data (Dye et al. subm., Enia et al., in prep.). We refer the reader to these references for more details.

In order to reduce computational time, not every pixel enters the modeling alghoritm, 