  Machine learning and many of its applications are considered hard to approach
due to their complexity and lack of transparency. One mission of human-centric
machine learning is to improve algorithm transparency and user satisfaction
while ensuring an acceptable task accuracy. In this work, we present an
interactive image restoration framework, which exploits both image prior and
human painting knowledge in an iterative manner such that they can boost on
each other. Additionally, in this system users can repeatedly get feedback of
their interactions from the restoration progress. This informs the users about
their impact on the restoration results, which leads to better sense of
control, which can lead to greater trust and approachability. The positive
results of both objective and subjective evaluation indicate that, our
interactive approach positively contributes to the approachability of
restoration algorithms in terms of algorithm performance and user experience.
