Open AccessImage fusion network for dual-modal restoration
Author(s) -
Ying Zhang,
Xuhua Ren,
Bryan Clifford,
Qian Wang,
Xiaoqun Zhang
Publication year - 2021
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2021067
Subject(s) - computer science , wavelet , artificial intelligence , modal , image fusion , image quality , image restoration , image (mathematics) , representation (politics) , computer vision , data mining , image processing , chemistry , politics , political science , polymer chemistry , law
In recent years multi-modal data processing methods have gained considerable research interest as technological advancements in imaging, computing, and data storage have made the collection of redundant, multi-modal data more commonplace. In this work we present an image restoration method tailored for scenarios where pre-existing, high-quality images from different modalities or contrasts are available in addition to the target image. Our method is based on a novel network architecture which combines the benefits of traditional multi-scale signal representation, such as wavelets, with more recent concepts from data fusion methods. Results from numerical simulations in which T1-weighted MRI images are used to restore noisy and undersampled T2-weighted images demonstrate that the proposed network successfully utilizes information from high-quality reference images to improve the restoration quality of the target image beyond that of existing popular methods.