# Learning a Discriminative Prior for Blind Image Deblurring

arXiv: https://arxiv.org/abs/1803.03363 · HTML: https://mlsanity.com/paper/1803.03363/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2018.

## Built on by (most cited first, 15 of 15)

- [Parallel Diffusion Models of Operator and Image for Blind Inverse Problems](https://mlsanity.com/paper/2211.10656/) (2022)
- [BALF: Simple and Efficient Blur Aware Local Feature Detector](https://mlsanity.com/paper/2211.14731/) (2022)
- [Face Deblurring using Dual Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2207.11617/) (2022)
- [Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring](https://mlsanity.com/paper/2104.12665/) (2021)
- [Blind Image Deblurring with Unknown Kernel Size and Substantial Noise](https://mlsanity.com/paper/2208.09483/) (2022)
- [Learning Lens Blur Fields](https://mlsanity.com/paper/2310.11535/) (2023)
- [Blind Image Deconvolution Using Variational Deep Image Prior](https://mlsanity.com/paper/2202.00179/) (2022)
- [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024)
- [Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring](https://mlsanity.com/paper/2204.12139/) (2022)
- [Towards Vision Transformer Unrolling Fixed-Point Algorithm: a Case Study on Image Restoration](https://mlsanity.com/paper/2301.12332/) (2023)
- [A Comprehensive Survey on Deep Neural Image Deblurring](https://mlsanity.com/paper/2310.04719/) (2023)
- [Blind Image Deblurring with FFT-ReLU Sparsity Prior](https://mlsanity.com/paper/2406.08344/) (2024)
- [Affine-modeled video extraction from a single motion blurred image](https://mlsanity.com/paper/2104.03777/) (2021)
- [SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning](https://mlsanity.com/paper/2111.04026/) (2021)
- [Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring](https://mlsanity.com/paper/2402.06117/) (2024)

## Abstract (arXiv)

We present an effective blind image deblurring method based on a data-driven discriminative prior.Our work is motivated by the fact that a good image prior should favor clear images over blurred images.In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN).The learned prior is able to distinguish whether an input image is clear or not.Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images.However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN.Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model.Furthermore, the proposed model can be easily extended to non-uniform deblurring.Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.