# Edge Heuristic GAN for Non-uniform Blind Deblurring

## TL;DR

This paper introduces an edge heuristic multi-scale GAN that leverages edge information and a hierarchical loss to effectively restore sharp images from non-uniform motion blurred images, outperforming existing methods.

## Contribution

It proposes a novel edge heuristic multi-scale GAN with an edge-enhanced network and hierarchical loss for improved non-uniform image deblurring.

## Key findings

- Achieves state-of-the-art results on dynamic scene deblurring datasets.
- Demonstrates superior performance over traditional kernel estimation methods.
- Effectively restores details in images with complex motion blur.

## Abstract

Non-uniform blur, mainly caused by camera shake and motions of multiple objects, is one of the most common causes of image quality degradation. However, the traditional blind deblurring methods based on blur kernel estimation do not perform well on complicated non-uniform motion blurs. Recent studies show that GAN-based approaches achieve impressive performance on deblurring tasks. In this letter, to further improve the performance of GAN-based methods on deblurring tasks, we propose an edge heuristic multi-scale generative adversarial network(GAN), which uses the "coarse-to-fine" scheme to restore clear images in an end-to-end manner. In particular, an edge-enhanced network is designed to generate sharp edges as auxiliary information to guide the deblurring process. Furthermore, We propose a hierarchical content loss function for deblurring tasks. Extensive experiments on different datasets show that our method achieves state-of-the-art performance in dynamic scene deblurring.