# DeepDeblur: Fast one-step blurry face images restoration

arXiv: https://arxiv.org/abs/1711.09515 · HTML: https://mlsanity.com/paper/1711.09515/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Real-World Deep Local Motion Deblurring](https://mlsanity.com/paper/2204.08179/) (2022)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02743/) (2021)
- [A survey on facial image deblurring](https://mlsanity.com/paper/2302.05017/) (2023)
- [Multi-view reconstruction of bullet time effect based on improved NSFF model](https://mlsanity.com/paper/2304.00330/) (2023)

## Abstract (arXiv)

We propose a very fast and effective one-step restoring method for blurry face images. In the last decades, many blind deblurring algorithms have been proposed to restore latent sharp images. However, these algorithms run slowly because of involving two steps: kernel estimation and following non-blind deconvolution or latent image estimation. Also they cannot handle face images in small size. Our proposed method restores sharp face images directly in one step using Convolutional Neural Network. Unlike previous deep learning involved methods that can only handle a single blur kernel at one time, our network is trained on totally random and numerous training sample pairs to deal with the variances due to different blur kernels in practice. A smoothness regularization as well as a facial regularization are added to keep facial identity information which is the key to face image applications. Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration. Moreover, the proposed method shows significant improvement in face recognition accuracy along with increasing running speed by more than 100 times.