# Deep Generative Filter for Motion Deblurring

arXiv: https://arxiv.org/abs/1709.03481 · HTML: https://mlsanity.com/paper/1709.03481/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 14 of 14)

- [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021)
- [EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration](https://mlsanity.com/paper/2105.04872/) (2021)
- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [Application of Ghost-DeblurGAN to Fiducial Marker Detection](https://mlsanity.com/paper/2109.03379/) (2021)
- [Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring](https://mlsanity.com/paper/2204.12139/) (2022)
- [A Dataset and Model for Realistic License Plate Deblurring](https://mlsanity.com/paper/2404.13677/) (2024)
- [A Comprehensive Survey on Deep Neural Image Deblurring](https://mlsanity.com/paper/2310.04719/) (2023)
- [Image restoration quality assessment based on regional differential information entropy](https://mlsanity.com/paper/2107.03642/) (2021)
- [Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks](https://mlsanity.com/paper/2103.02121/) (2021)
- [A General Divergence Modeling Strategy for Salient Object Detection](https://mlsanity.com/paper/2111.11827/) (2021)
- [FMD-cGAN: Fast Motion Deblurring using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2111.15438/) (2021)
- [A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation](https://mlsanity.com/paper/2208.10711/) (2022)
- [Deep-Learning-Assisted Analysis of Cataract Surgery Videos](https://mlsanity.com/paper/2312.05900/) (2023)
- [VDPI: Video Deblurring with Pseudo-inverse Modeling](https://mlsanity.com/paper/2409.00777/) (2024)

## Abstract (arXiv)

Removing blur caused by camera shake in images has always been a challenging problem in computer vision literature due to its ill-posed nature. Motion blur caused due to the relative motion between the camera and the object in 3D space induces a spatially varying blurring effect over the entire image. In this paper, we propose a novel deep filter based on Generative Adversarial Network (GAN) architecture integrated with global skip connection and dense architecture in order to tackle this problem. Our model, while bypassing the process of blur kernel estimation, significantly reduces the test time which is necessary for practical applications. The experiments on the benchmark datasets prove the effectiveness of the proposed method which outperforms the state-of-the-art blind deblurring algorithms both quantitatively and qualitatively.