# GAN Based Image Deblurring Using Dark Channel Prior

## TL;DR

This paper introduces a GAN-based image deblurring method that incorporates dark channel prior into the loss function, resulting in improved performance, robustness to noise, and a lightweight network architecture.

## Contribution

It presents a novel GAN framework for image deblurring that integrates dark channel prior into the loss function, replacing the indifferentiable form with an L2 norm for compatibility.

## Key findings

- Improved deblurring quality on synthetic and real images
- Enhanced robustness to image noise
- Lighter network structure reduces training and testing time

## Abstract

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.