# Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations

arXiv: https://arxiv.org/abs/1909.03748 · HTML: https://mlsanity.com/paper/1909.03748/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2404.09790/) (2024)
- [A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution](https://mlsanity.com/paper/2107.03145/) (2021)
- [RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network](https://mlsanity.com/paper/2110.13217/) (2021)
- [Real Image Super-Resolution using GAN through modeling of LR and HR process](https://mlsanity.com/paper/2210.10413/) (2022)

## Abstract (arXiv)

Single Image Super-Resolution (SISR) aims to generate a high-resolution (HR) image of a given low-resolution (LR) image. The most of existing convolutional neural network (CNN) based SISR methods usually take an assumption that a LR image is only bicubicly down-sampled version of an HR image. However, the true degradation (i.e. the LR image is a bicubicly downsampled, blurred and noisy version of an HR image) of a LR image goes beyond the widely used bicubic assumption, which makes the SISR problem highly ill-posed nature of inverse problems. To address this issue, we propose a deep SISR network that works for blur kernels of different sizes, and different noise levels in an unified residual CNN-based denoiser network, which significantly improves a practical CNN-based super-resolver for real applications. Extensive experimental results on synthetic LR datasets and real images demonstrate that our proposed method not only can produce better results on more realistic degradation but also computational efficient to practical SISR applications.