# Multi-scale deep neural networks for real image super-resolution

arXiv: https://arxiv.org/abs/1904.10698 · HTML: https://mlsanity.com/paper/1904.10698/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [SeD: Semantic-Aware Discriminator for Image Super-Resolution](https://mlsanity.com/paper/2402.19387/) (2024)
- [NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study](https://mlsanity.com/paper/2504.15003/) (2025)
- [Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction](https://mlsanity.com/paper/2501.09049/) (2025)
- [Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network](https://mlsanity.com/paper/2211.10563/) (2022)
- [Gradient-Free Adversarial Purification with Diffusion Models](https://mlsanity.com/paper/2501.13336/) (2025)
- [SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution](https://mlsanity.com/paper/2211.12180/) (2022)
- [HeadsUp! High-Fidelity Portrait Image Super-Resolution](https://mlsanity.com/paper/2510.09924/) (2025)

## Abstract (arXiv)

Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work. Firstly, due to the high computation complexity in high-resolution spaces, we process an input image mainly in two different downscaling spaces, which could greatly lower the usage of GPU memory. Then, to reconstruct the details of an image, we design a multi-scale residual network (MsRN) in the downscaling spaces based on the residual blocks. Besides, we propose a multi-scale dense network based on the dense blocks to compare with MsRN. Finally, our empirical experiments show the robustness of MsDNN for image SR when the upscaling factor is unknown. According to the preliminary results of NTIRE 2019 image SR challenge, our team (ZXHresearch@fudan) ranks 21-st among all participants. The implementation of MsDNN is released https://github.com/shangqigao/gsq-image-SR