Khowaja, Sunder Ali ORCID: 0000-0002-4586-4131, Ali, Usman, Dev, Kapal and Lee, Ik Hyun
(2025)
Image Registration and Deep NeuroFuzzy Networks for Mitigating Atmospheric Turbulence Effects in Consumer-Based Optical Imaging.
IEEE Transactions on Consumer Electronics
.
pp. 1-10.
ISSN 1558-4127
Abstract
Consumer-based optical imaging systems are characterized as big data processing systems, which are drastically affected by atmospheric turbulences that add geometric distortions and blur effect to the images when used in outdoor condition. Physics-grounded simulators have been proposed recently to generate synthetic data but the generalization to the real-world turbulent images is not so good. In this paper, we combine the characteristics of image registration, deep neurofuzzy methods, and channel-attention based discriminative learning strategy to propose image registration, neurofuzzy based denoising, and deblurring network (RND2Net). The RND2Net is designed on a principle that it does not require turbulent image pairs (ground truth images) to train the network, which closely resembles the real-world situation used as consumer devices. The registration module focuses on the region-based fusion techniques while the denoising and deblurring module incorporates deep neurofuzzy network along with dense residual blocks and channel attention mechanism to train the network. The RND2Net is also designed to reduce the noise and blur effect from images, while generalizing on the down-stream tasks, such as text recognition. Experimental results show that the RND2Net yields better performance quantitatively as qualitatively on synthetic and real-world datasets in comparison to existing state-of-the-art methods.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | Deep NeuroFuzzy Networks, Big Data Processing, Image Restoration, Atmospheric Turbulence, Channel Attention, Image Registration |
Subjects: | Computer Science > Computer engineering Computer Science > Computer software |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
Publisher: | Institute of Electrical and Electronics Engineers |
Official URL: | https://ieeexplore.ieee.org/document/10838578/ |
Copyright Information: | Authors |
ID Code: | 30696 |
Deposited On: | 04 Feb 2025 11:28 by Vidatum Academic . Last Modified 04 Feb 2025 11:28 |
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