End-to-end NAS-SR methodology orchestrates four stages: supernet training on DIV2K and Flickr2K datasets, architecture search via random sampling and reinforcement learning with PSNR/SSIM rewards, final network extraction and fine-tuning with Charbonnier loss, and multi-dataset benchmark evaluation on Set5, Set14, BSD100, Urban100, and Manga109. The pipeline leverages shared weights across searchable blocks to reduce computational overhead while exploring candidate architectures. This approach balances image quality metrics (PSNR, SSIM, LPIPS) against FLOPs penalties and architectural diversity, enabling practitioners to discover efficient super-resolution models without exhaustive training. Fork and customize this flowchart to adapt the search space, reward function, or benchmark datasets for your specific super-resolution task.