Image fusion network for dual-modal restoration

IF 1.2 4区数学 Q2 MATHEMATICS, APPLIED

Inverse Problems and Imaging Pub Date : 2021-01-01 DOI:10.3934/ipi.2021067

Ying Zhang, Xuhua Ren, B. Clifford, Qian Wang, Xiaoqun Zhang

引用次数: 0

Abstract

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.

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双峰恢复图像融合网络

近年来，随着成像、计算和数据存储技术的进步，使得冗余、多模态数据的收集变得更加普遍，多模态数据处理方法获得了相当大的研究兴趣。在这项工作中，我们提出了一种图像恢复方法，适用于除了目标图像之外，还可以获得来自不同模态或对比度的预先存在的高质量图像的场景。我们的方法是基于一种新的网络架构，它结合了传统的多尺度信号表示(如小波)的优点和数据融合方法的最新概念。利用t1加权MRI图像恢复有噪声和欠采样的t2加权图像的数值模拟结果表明，所提出的网络成功地利用了高质量参考图像的信息，提高了目标图像的恢复质量，超过了现有的流行方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Inverse Problems and Imaging 数学-物理：数学物理

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Inverse Problems and Imaging publishes research articles of the highest quality that employ innovative mathematical and modeling techniques to study inverse and imaging problems arising in engineering and other sciences. Every published paper has a strong mathematical orientation employing methods from such areas as control theory, discrete mathematics, differential geometry, harmonic analysis, functional analysis, integral geometry, mathematical physics, numerical analysis, optimization, partial differential equations, and stochastic and statistical methods. The field of applications includes medical and other imaging, nondestructive testing, geophysical prospection and remote sensing as well as image analysis and image processing. This journal is committed to recording important new results in its field and will maintain the highest standards of innovation and quality. To be published in this journal, a paper must be correct, novel, nontrivial and of interest to a substantial number of researchers and readers.