# Semantically Accurate Super-Resolution Generative Adversarial Networks Source: [Semantically Accurate Super-Resolution Generative Adversarial Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:14:46.163Z Authors: Tristan Frizza, Donald G. Dansereau, Nagita Mehr Seresht, Michael Bewley Published: 2022-05-17T23:05:27.000Z ## Abstract This work addresses the problems of semantic segmentation and image super-resolution by jointly considering the performance of both in training a Generative Adversarial Network (GAN). We propose a novel architecture and domain-specific feature loss, allowing super-resolution to operate as a pre-processing step to increase the performance of downstream computer vision tasks, specifically semantic segmentation. We demonstrate this approach using Nearmap's aerial imagery dataset which covers hundreds of urban areas at 5-7 cm per pixel resolution. We show the proposed approach improves perceived image quality as well as quantitative segmentation accuracy across all prediction classes, yielding an average accuracy improvement of 11.8% and 108% at 4x and 32x super-resolution, compared with state-of-the art single-network methods. This work demonstrates that jointly considering image-based and task-specific losses can improve the performance of both, and advances the state-of-the-art in semantic-aware super-resolution of aerial imagery. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on NYU Depth V2 (test)](): Semantic segmentation; NYU Depth V2 (test); mIoU: 16.5. ## Original sources and implementation [Original paper]() [PDF]()