Mr. Joonhyuk Park Profile

Joonhyuk Park

at Handong Global Univ

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 | 10 March 2020 Paper

A grid-line suppression technique based on deep convolutional neural networks

Kyongwoo Kim, Hyungkyu Kim, Heesin Lee, Joongeun Jung, Joshua Nam, Joonhyuk Park, Donghyun Kim, Hyewon Kim, Hojoon Kim

Proceedings Volume 11313, 1131327 (2020) https://doi.org/10.1117/12.2549281

KEYWORDS: Linear filtering, Convolutional neural networks, X-ray imaging, X-rays, Image filtering, Data modeling, Radiography, Image restoration, Detection and tracking algorithms, Image enhancement

Read Abstract +

Anti-scatter grids are routinely used to prevent degradation of image quality caused by scattered X-ray beams. However, these grids might cause linear artifacts that represent the shadows of the radiopaque septa. In this paper, we propose a machine learning-based method for grid artifact suppression in radiography. There are two major difficulties in the application of a deep learning technique for the grid-line suppression problem. The first is quantitative shortage of learning data. It is difficult to acquire a sufficient amount of learning data from observed images in consideration of the various situations in which grid lines appear. The second is difficulty determining the target data. It is practically impossible to generate a clean target image for an arbitrary grid-line image. To overcome these problems, we propose a deep convolutional neural network architecture and a learning data construction method. A discrete cosine transform-based band-stop filtering technique and an image synthesizing algorithm were adopted for the learning data construction method. A patch sampling method was employed to overcome the shortage of the amount of learning data. The proposed method enables learning without clean target data and overcomes the weakness of conventional frequency analysis-based methods with regard to the grid-line suppression problem. This method makes it possible to expect complementary performance through the construction of a combined learning data set including observed images and artificially generated grid-line images.

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