Edge Heuristic GAN for Non-uniform Blind Deblurring
This addresses image quality degradation from camera shake and object motions, offering an incremental improvement for computer vision applications.
The paper tackled non-uniform blind deblurring in images, proposing an edge heuristic multi-scale GAN that uses sharp edges as guidance and a hierarchical content loss, achieving state-of-the-art performance on dynamic scene datasets.
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.