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A key challenge of high contrast imaging (HCI) is to differentiate a specklefrom an exoplanet signal. The sources of speckles are a combination ofatmospheric residuals and aberrations in the non-common path. Those non-commonpath aberrations (NCPA) are particularly challenging to compensate for as theyare not directly measured, and because they include static, quasi-static anddynamic components. The proposed method directly addresses the challenge ofcompensating the NCPA. The algorithm DrWHO - Direct Reinforcement WavefrontHeuristic Optimisation - is a quasi-real-time compensation of static anddynamic NCPA for boosting image contrast. It is an image-based lucky imagingapproach, aimed at finding and continuously updating the ideal reference of thewavefront sensor (WFS) that includes the NCPA, and updating this new referenceto the WFS. Doing so changes the point of convergence of the AO loop. We showhere the first results of a post-coronagraphic application of DrWHO. DrWHO doesnot rely on any model nor requires accurate wavefront sensor calibration, andis applicable to non-linear wavefront sensing situations. We present on-skyperformances using a pyramid WFS sensor with the Subaru coronagraph extreme AO(SCExAO) instrument.

High contrast imaging wavefront sensor referencing from coronagraphic images