  In real-life applications, certain images utilized are corrupted in which the
image pixels are damaged or missing, which increases the complexity of computer
vision tasks. In this paper, a deep learning architecture is proposed to deal
with image completion and enhancement. Generative Adversarial Networks (GAN),
has been turned out to be helpful in picture completion tasks. Therefore, in
GANs, Wasserstein GAN architecture is used for image completion which creates
the coarse patches to filling the missing region in the distorted picture, and
the enhancement network will additionally refine the resultant pictures
utilizing residual learning procedures and hence give better complete pictures
for computer vision applications. Experimental outcomes show that the proposed
approach improves the Peak Signal to Noise ratio and Structural Similarity
Index values by 2.45% and 4% respectively when compared to the recently
reported data.
