Deep learning for terahertz image denoising in nondestructive historical document analysis

12Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.

References Powered by Scopus

Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

14574Citations
N/AReaders
Get full text

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

8366Citations
N/AReaders
Get full text

Making a 'completely blind' image quality analyzer

4914Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Nondestructive Evaluation of Lined Paintings by THz Pulsed Time-Domain Imaging

8Citations
N/AReaders
Get full text

Review of deep learning-based methods for non-destructive evaluation of agricultural products

7Citations
N/AReaders
Get full text

Review of Bioplastics Characterisation by Terahertz Techniques in the View of Ensuring a Circular Economy

5Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Dutta, B., Root, K., Ullmann, I., Wagner, F., Mayr, M., Seuret, M., … Huang, Y. (2022). Deep learning for terahertz image denoising in nondestructive historical document analysis. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-26957-7

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

38%

Lecturer / Post doc 2

25%

Researcher 2

25%

Professor / Associate Prof. 1

13%

Readers' Discipline

Tooltip

Physics and Astronomy 4

40%

Engineering 4

40%

Computer Science 2

20%

Save time finding and organizing research with Mendeley

Sign up for free