Mr. Geun-Young Oh Profile

Geun-Young Oh

at Picopack Inc

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 | 4 April 2022 Poster + Paper

Single-image high-dynamic-range reconstruction for intraoral dental x-ray images

Sunghoon Choi, Jin-Woo Jeong, Geun-Young Oh, Dong-Il Kim, Jin-Gyu Yang, Yoon-Ho Song

Proceedings Volume 12031, 120314G (2022) https://doi.org/10.1117/12.2611616

KEYWORDS: High dynamic range imaging, X-ray imaging

Read Abstract +

Intraoral dental x-ray exams are the cost-effective way for early detection of decay between teeth. The scintillation-based x-ray detectors coupled with fiber optic plate (FOP) with CMOS sensor are well-suited for high-resolution intraoral radiography. However, the transmitted x-ray photons, which are not absorbed in the FOP, directly hit the end of CMOS sensors, thus results in the salt and pepper noise on the image. Another issue is the optimization of contrast and brightness when the foreign bodies exist in tooth. A standard tone-mapping operator (TMO) utilizes a full scale of 14-bit gray range so that a whole pixel values including the outlier (high density materials) are distributed over the fixed range. This would degrade the contrast of the soft tissue because the TMO is still limited to low dynamic range (LDR) and associated with the varying parameters for the pixel value correction. A deep neural network-based approach was proposed to expand the LDR image to synthetic high dynamic range (HDR) image in order to enhance the contrast of the soft tissue after the TMO. We have also conducted the adaptive median filtering to reject the salt and pepper noise on the images before the network training. We firstly added the salt and pepper noise on the open camera dataset (24-bit color RGB) and then validated our method using the periapical intraoral dental x-ray images. The results indicated that the noise-corrupted LDR images were optimally reconstructed into HDR images for both simulation and experiment dataset. The tone-mapped LDR from the synthetic HDR images showed the enhanced PSNRs by factors of 1.08 – 1.21 by comparing the unprocessed LDR images. In conclusion, the proposed parameter-free deep learning method may provide better outcomes in practical intraoral radiography.

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