Semantic Image Completion and Enhancement using Deep Learning

TL;DR

This paper introduces a deep learning method using Wasserstein GANs and residual learning to improve image completion and enhancement, resulting in higher quality images for computer vision tasks.

Contribution

It presents a novel deep learning architecture combining Wasserstein GANs and residual learning for improved image completion and enhancement.

Findings

Peak Signal to Noise ratio increased by 2.45%

Structural Similarity Index improved by 4%

Outperforms recent methods in image quality metrics

Abstract

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.