Mr. Yi Luo Profile

Yi Luo

at Tsinghua Univ Shenzhen International Graduate School

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 February 2021 Presentation + Paper

Single MR image super-resolution via mixed self-similarity attention network

Xiaowan Hu, Haoqian Wang, Yi Luo, Zhongzhi Sun, Yanbin Peng

Proceedings Volume 11596, 1159605 (2021) https://doi.org/10.1117/12.2580860

KEYWORDS: Magnetic resonance imaging, Super resolution, Medical imaging, Visualization, Magnetism, Lawrencium, Image restoration

Read Abstract +

The single-image super-resolution (SISR) network based on deep learning is dedicated to learning the mapping between low-resolution (LR) images and high-resolution (HR) images. The optimal parameters of these networks often require extensive training on large-scale external image databases. For medical magnetic resonance (MR) images, there is a lack of large data sets containing high-quality images. Some deep networks that perform well on natural images cannot be fully trained on MR images, which limits the super-resolution (SR) performance. In traditional methods, the non-local self-similarity has been verified as useful statistical prior information for image restoration. The inherent feature correlation not only exists between pixels, but some patches also tend to be repeated at different positions within and across scales of MR images. Therefore, in this paper, we propose a mixed self-similarity attention network (MSAN) to explore the long-range dependencies of different regions fully. In the feature map of the entire input MR image, the prior information of self-similarity is divided into two scales: point-similarity and patch-similarity. We use points and patches that are highly similar to the current area to restore a more detailed structural texture. The internal correlation items can be used as an essential supplement to the limited external training dataset. Besides, the large number of less informative background in MR images will interfere with practical self-similarity information. A dual attention mechanism combining first-order attention and second-order attention gives more weight to salient features and suppresses the activation of useless features. Comprehensive experiments demonstrate that the proposed achieves significantly superior results on MR images SR while outperforming state-of-the-art methods by a large margin quantitatively and visually.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years