TexSR: Image Super-Resolution for High-Quality Texture Mapping
Jae‐Ho Nah, Hyeju Kim · 2022
We introduce an image super-resolution technique for high-quality texture mapping in this poster. We first get upscaled textures from an existing image super-resolution (SR) method. We then perform a post-color correction algorithm to restore color tones and details lost in the SR algorithm. Finally, we compress the textures with variable compression ratios to reduce storage and memory overheads caused by the increased resolution. As a result, TexSR can improve the image quality of a state of the art, Real-ESRGAN.