AI in Photography: Scrutinizing Implementation of Super-Resolution Techniques in Photo-Editors

Judging the quality of a photograph from the perspective of a photographer we can ascertain resolution, symmetry, content, location, etc. as some of the factors that influence the proficiency of a photograph. The exponential growth in the allurement for photography impels us to discover ways to perfect an input image in terms of the aforesaid parameters. Where content and location are the immutable ones, attributes like symmetry and resolution can be worked upon. In this paper, I prioritized resolution as our cynosure and there can be multiple ways to refine it. Image super-resolution is progressively becoming a prerequisite in the fraternity of computer graphics, computer vision, and image processing. It's the process of obtaining high-resolution images from their low-resolution counterparts. In my work, image super-resolution techniques like 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 studied experimentally for post-enhancement of images in photography as employed by photo-editors, establishing the most coherent approach for attaining optimized super-resolution in terms of quality.