Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network
This work addresses image quality enhancement for applications like medical imaging and astronomy, but it appears incremental as it builds on existing GAN-based super-resolution techniques.
The paper tackles the problem of generating higher-resolution images from lower-resolution inputs using a Residual in Residual Dense Block network architecture, achieving better visual quality compared to other methods.
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.