Lunar Turing-GAN Super-Resolution Model Objective: Create a high-resolution map of the Moon using a pipeline of Image Super-Resolution models Proposed a novel GAN-based architecture with turing test based adversaries for ensuring accurate reconstruction of craters and hills. Achieved a competitive SSIM of 0.794 while increasing image spatial resolution from 5m per pixel to 30 cm per pixel, a 16x magnification. Created a pipeline capable of tiling and super-resolving an image using Lunar T-GAN, HAT, RealESRGAN and sharpening algorithms....