EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Author:
Affiliation:
1. Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, USA
2. Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
Funder
NSF Data Science
Data Analytics that are Robust and Trusted
Arkansas Biosciences Institute (ABI) Grant Program
NSF West Virginia Arkansas-Collaborative Research and Education in Smart Health (WVAR-CRESH) Grant
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/6287639/9668973/9874820-aam.pdf
Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. VAEWGAN-NCO in image deblurring framework using variational autoencoders and Wasserstein generative adversarial network;Signal, Image and Video Processing;2024-03-25
2. Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction;IEEE Access;2024
3. Multi-Path Attention Block-Loss Function for Non-Uniform Blind Image Deblurring;2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC);2023-12-04
4. FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
5. The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation;IEEE Access;2023
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