A lightweight neural network that upscales 128×128 RGB images to 256×256. No deep learning frameworks or image libraries required. Python 3.10+. Both comparisons use the best weights from 100 training epochs. Originals are displayed at 2× size. Held-out validation PSNR: 26.75 dB, compared with 25.05 dB for nearest-neighbor upscaling. Install and upscale using the included weights: Saves the 256×256 output and a labeled outputs/upscaled-comparison.png. Inputs must be non-interlaced, 8-bit PNGs; convert WebP images to PNG first. The included superressamples/ dataset contains 199 image pairs, with matching filenames in 128x128/ and 256x256/. Train a new model: Training uses random aligned…