Comparative analysis of the deep-learning-based super-resolution methods for generating high-resolution texture maps

Abstract

As display resolution increases, many apps also tend to include high-resolution texture maps. Recent advancements in deeplearning-based image super-resolution techniques make it possible to automate high-resolution texture generation. However, there is still a lack of comprehensive analysis of the application of these techniques to texture maps. In this paper, we selected three recent super-resolution techniques, namely BSRGAN, Real-ESRGAN, and SwinIR (classical and real-world image SR), and applied them to upscale texture maps. We then conducted a quantitative and qualitative analysis of the experimental results. The findings revealed various artifacts after upscaling, which indicates that there are still limitations in directly applying superresolution techniques to texture-map upscaling.