# Generative adversarial network-based image super-resolution using perceptual content losses

arXiv: https://arxiv.org/abs/1809.04783 · HTML: https://mlsanity.com/paper/1809.04783/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [Perceptual Image Quality Assessment with Transformers](https://mlsanity.com/paper/2104.14730/) (2021)
- [Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition](https://mlsanity.com/paper/2303.04291/) (2023)
- [Conformer and Blind Noisy Students for Improved Image Quality Assessment](https://mlsanity.com/paper/2204.12819/) (2022)
- [Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem](https://mlsanity.com/paper/2409.03179/) (2024)
- [MGHF: Multi-Granular High-Frequency Perceptual Loss for Image Super-Resolution](https://mlsanity.com/paper/2411.13548/) (2024)
- [Multi-Grid Back-Projection Networks](https://mlsanity.com/paper/2101.00150/) (2021)
- [A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models](https://mlsanity.com/paper/2403.10589/) (2024)

## Abstract (arXiv)

In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a recently developed model for super-resolution, i.e., deep residual network using enhanced upscale modules (EUSR), the proposed model is trained to improve perceptual performance with only slight increase of distortion. For this purpose, together with the conventional content loss, i.e., reconstruction loss such as L1 or L2, we consider additional losses in the training phase, which are the discrete cosine transform coefficients loss and differential content loss. These consider perceptual part in the content loss, i.e., consideration of proper high frequency components is helpful for the trade-off problem in super-resolution. The experimental results show that our proposed model has good performance for both perception and distortion, and is effective in perceptual super-resolution applications.