  Image inpainting is a key technique in image processing task to predict the
missing regions and generate realistic images. Given the advancement of
existing generative inpainting models with feature extraction, propagation and
reconstruction capabilities, there is lack of high-quality feature extraction
and transfer mechanisms in deeper layers to tackle persistent aberrations on
the generated inpainted regions. Our method, V-LinkNet, develops high-level
feature transference to deep level textural context of inpainted regions our
work, proposes a novel technique of combining encoders learning through a
recursive residual transition layer (RSTL). The RSTL layer easily adapts dual
encoders by increasing the unique semantic information through direct
communication. By collaborating the dual encoders structure with contextualised
feature representation loss function, our system gains the ability to inpaint
with high-level features. To reduce biases from random mask-image pairing, we
introduce a standard protocol with paired mask-image on the testing set of
CelebA-HQ, Paris Street View and Places2 datasets. Our results show V-LinkNet
performed better on CelebA-HQ and Paris Street View using this standard
protocol. We will share the standard protocol and our codes with the research
community upon acceptance of this paper.
