Abstract:
Image inpainting is nowadays demanding because
of its wide applications such as removing the unwanted objects
from the image or recovering the old corrupted photo. Existing
approaches achieved superior performance with coarse-to-fine or
progressive or recurrent architectures for image inpainting regardless of computational complexity. In these types, the disturbance
at the first instance or first iteration may lead to semantically
unambiguous results. Also, to inpaint the image with varying hole
sizes it is desirable to focus on the diverse receptive fields without deeper network i.e, network with less number of parameters.
Therefore, we have proposed a lightweight adversarial concurrent
encoder architecture with a diverse receptive field for image inpainting. Here, the concurrent encoder is integrated with diverse
receptive fields to benefit with lower computational complexity.
The proposed method is compared with state-of-the-art (SOTA)
methods on Places2 and Paris Street View dataset in terms of peak
signal-to-noise ratio and structural similarity index. Along with the
extensive results’ analysis and ablation study, the proposed method
proves the effectiveness in terms of less computational complexity
compared to existing SOTA methods.
