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Liu, Y, Fang, F, Wang, T, Li, J, Sheng, Y and Zhang, G (2021) Multi-scale Grid Network for Image Deblurring with High-frequency Guidance. The IEEE Transactions on Multimedia. ISSN 1520-9210
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Abstract
It has been demonstrated that the blurring process reduces the high-frequency information of the original sharp image, so the main challenge for image deblurring is to reconstruct high-frequency information from the blurry image. In this paper, we propose a novel image deblurring framework to focus on the reconstruction of high-frequency information, which consists of two main subnetworks: a high-frequency reconstruction subnetwork (HFRSN) and a multi-scale grid subnetwork (MSGSN). The HFRSN is built to reconstruct latent high-frequency information from multiple scale blurry images. The MSGSN performs deblurring processes with high-frequency guidance at different scales simultaneously. Besides, in order to better use high-frequency information to restore sharpening images, we designed a high-frequency information aggregation (HFAG) module and a high-frequency information attention (HFAT) module in MSGSN. The HFAG module is designed to fuse high-frequency features and image features at the feature extraction stage, and the HFAT module is built to enhance the feature reconstruction stage. Extensive experiments on different datasets show the effectiveness and efficiency of our method.
| Item Type: | Article |
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| Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | 08 Information and Computing Sciences, 09 Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Date Deposited: | 05 Jul 2021 09:52 |
| Last Modified: | 02 Mar 2022 09:14 |
| DOI or ID number: | 10.1109/TMM.2021.3090206 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15214 |
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