An ADMM method for diffuse optical tomography with L2 and L∞ fidelity and total variation regularization

Junlong Tao; Yuting Wei; Bo Bi

doi:10.1117/12.3045263

9 October 2024 An ADMM method for diffuse optical tomography with L2 and L∞ fidelity and total variation regularization

Junlong Tao, Yuting Wei, Bo Bi

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 1328818 (2024) https://doi.org/10.1117/12.3045263
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

This study introduces a Diffuse Optical Tomography reconstruction method that employs both L2 and L∞ fidelity and total variation regularization, solved by the ADMM method. By incorporating L∞ and L2 fidelity to mitigate the effects of uniform and Gaussian noise, along with TV regularization to enhance edge definition and structural integrity, our method effectively addresses the challenges associated with the ill-posed nature of DOT reconstructions. Numerical results shows that our approach can reconstruct satisfactory images under different noise levels.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junlong Tao, Yuting Wei, and Bo Bi "An ADMM method for diffuse optical tomography with L2 and L∞ fidelity and total variation regularization", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 1328818 (9 October 2024); https://doi.org/10.1117/12.3045263

ACCESS THE FULL ARTICLE

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