# GAN Based Image Deblurring Using Dark Channel Prior

arXiv: https://arxiv.org/abs/1903.00107 · HTML: https://mlsanity.com/paper/1903.00107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Wiener Guided DIP for Unsupervised Blind Image Deconvolution](https://mlsanity.com/paper/2112.10271/) (2021)

## Abstract (arXiv)

A conditional general adversarial network (GAN) is proposed for image deblurring problem. It is tailored for image deblurring instead of just applying GAN on the deblurring problem. Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training. To make it more compatible with neuron networks, its original indifferentiable form is discarded and L2 norm is adopted instead. On both synthetic datasets and noisy natural images, the proposed network shows improved deblurring performance and robustness to image noise qualitatively and quantitatively. Additionally, compared to the existing end-to-end deblurring networks, our network structure is light-weight, which ensures less training and testing time.