Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network

TL;DR

This paper proposes a novel deep learning method using Residual in Residual Dense Block architecture to enhance image resolution, demonstrating superior visual quality compared to existing techniques.

Contribution

Introduces a new deep network architecture for single image super-resolution that outperforms previous methods in visual quality.

Findings

Our method produces higher quality images with clearer details.

The proposed approach outperforms existing super-resolution techniques.

Visual comparisons show improved sharpness and detail preservation.

Abstract

The resolution of an image is a very important criterion for evaluating the quality of the image. A higher resolution of an image is always preferable as images of lower resolution are unsuitable due to fuzzy quality. A higher resolution of an image is important for various fields such as medical imaging; astronomy works and so on as images of lower resolution becomes unclear and indistinct when their sizes are enlarged. In recent times, various research works are performed to generate a higher resolution of an image from its lower resolution. In this paper, we have proposed a technique of generating higher resolution images form lower resolution using Residual in Residual Dense Block network architecture with a deep network. We have also compared our method with other methods to prove that our method provides better visual quality images.