Open AccessEQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Author(s) -
Ibsa Jalata,
Naga Venkata Sai Raviteja Chappa,
Thanh-Dat Truong,
Pierce Helton,
Chase Rainwater,
Khoa Luu
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3203736
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In this paper, we present an end-to-end unsupervised domain adaptation approach to image deblurring. This work focuses on learning and generalizing the complex latent space of the source domain and transferring the extracted information to the unlabeled target domain. While fully supervised image deblurring methods have achieved high accuracy on large-scale vision datasets, they are unable to well generalize well on a new test environment or a new domain. Therefore, in this work, we introduce a novel Bijective Maximum Likelihood loss for the unsupervised domain adaptation approach to image deblurring. We evaluate our proposed method on GoPro, RealBlur_J, RealBlur_R, and HIDE datasets. Through intensive experiments, we demonstrate our state-of-the-art performance on the standard benchmarks.