Open AccessDeblurring Method for Motion Blurred Images based on GAN
Author(s) -
Ning Li,
Songnan Chen,
Mengxia Tang,
Jiangming Kan
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.12
Subject(s) - deblurring , computer science , image restoration , artificial intelligence , computer vision , motion blur , image (mathematics) , motion (physics) , a priori and a posteriori , metric (unit) , task (project management) , motion estimation , image processing , engineering , philosophy , operations management , systems engineering , epistemology
The purpose of image motion deblur is to recover the underlying clear image from the corresponding blur image. In most traditional methods, the image recovery task is formulated as a problem of blur core estimation and use a priori to calculate. In this paper we proposes a generative adversarial network(GAN) model based on the mobilenet-V3 network structure to meet the needs of motion blurred image recovery on mobile devices. Based on traditional evaluation indicators, we propose a new evaluation metric on mobile device. Extensive experiments show that our method is superior to the competing methods.