Publication

Abstract : We can determine resolution, symmetry, content, location, and other factors by judging the quality of a photograph from the perspective of a photographer. as well as some of the other elements that influence a photograph's quality. The ever-increasing allure of photography compels us to find new ways to improve an input image in terms of the mentioned parameters. While content and placement are unchangeable, qualities like as symmetry and resolution can be improved. I prioritised resolution as our cynosure in this research, and there are various methods to refine it. In the fields of computer graphics, computer vision, and image processing, image super-resolution is increasingly becoming a requirement. It's the process of converting low-resolution photos into high-resolution ones. Image super-resolution techniques such as Interpolation, SRCNN (Super-Resolution Convolutional Neural Network), SRResNet (Super Resolution Residual Network), and GANs (Generative Adversarial Networks: Super-Resolution GAN- SRGAN and Conditional GAN- CGAN) were investigated experimentally for post-enhancement of images in photography as used by photo-editors in my research. determining the most coherent technique for achieving quality-optimized super-resolution.