  Medical images often contain artificial markers added by doctors, which can
negatively affect the accuracy of AI-based diagnosis. To address this issue and
recover the missing visual contents, inpainting techniques are highly needed.
However, existing inpainting methods require manual mask input, limiting their
application scenarios. In this paper, we introduce a novel blind inpainting
method that automatically completes visual contents without specifying masks
for target areas in an image. Our proposed model includes a mask-free
reconstruction network and an object-aware discriminator. The reconstruction
network consists of two branches that predict the corrupted regions with
artificial markers and simultaneously recover the missing visual contents. The
object-aware discriminator relies on the powerful recognition capabilities of
the dense object detector to ensure that the markers of reconstructed images
cannot be detected in any local regions. As a result, the reconstructed image
can be close to the clean one as much as possible. Our proposed method is
evaluated on different medical image datasets, covering multiple imaging
modalities such as ultrasound (US), magnetic resonance imaging (MRI), and
electron microscopy (EM), demonstrating that our method is effective and robust
against various unknown missing region patterns.
