# Gated Fusion Network for Joint Image Deblurring and Super-Resolution

arXiv: https://arxiv.org/abs/1807.10806 · HTML: https://mlsanity.com/paper/1807.10806/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 16 of 16)

- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [NTIRE 2021 Challenge on Image Deblurring](https://mlsanity.com/paper/2104.14854/) (2021)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring](https://mlsanity.com/paper/2112.05150/) (2021)
- [Asymmetric CNN for image super-resolution](https://mlsanity.com/paper/2103.13634/) (2021)
- [HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors](https://mlsanity.com/paper/2103.10982/) (2021)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration](https://mlsanity.com/paper/2105.04872/) (2021)
- [Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding](https://mlsanity.com/paper/2105.13077/) (2021)
- [Blind Image Deconvolution Using Variational Deep Image Prior](https://mlsanity.com/paper/2202.00179/) (2022)
- [A Gated Fusion Network for Dynamic Saliency Prediction](https://mlsanity.com/paper/2102.07682/) (2021)
- [Training Neural Networks on RAW and HDR Images for Restoration Tasks](https://mlsanity.com/paper/2312.03640/) (2023)
- [Multimodal Sleep Apnea Detection with Missing or Noisy Modalities](https://mlsanity.com/paper/2402.17788/) (2024)
- [Multitask Learning for VVC Quality Enhancement and Super-Resolution](https://mlsanity.com/paper/2104.08319/) (2021)
- [SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning](https://mlsanity.com/paper/2111.04026/) (2021)
- [SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images](https://mlsanity.com/paper/2201.05865/) (2022)

## Abstract (arXiv)

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.