Next Article in Journal
Blast Damage Assessment of Symmetrical Box-Shaped Underground Tunnel According to Peak Particle Velocity (PPV) and Single Degree of Freedom (SDOF) Criteria
Previous Article in Journal
Symmetry Breakings in Dual-Core Systems with Double-Spot Localization of Nonlinearity
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(5), 157; https://doi.org/10.3390/sym10050157

Total Variation Based Neural Network Regression for Nonuniformity Correction of Infrared Images

Department of Microelectronics, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Received: 26 March 2018 / Revised: 3 May 2018 / Accepted: 12 May 2018 / Published: 14 May 2018
Full-Text   |   PDF [5783 KB, uploaded 14 May 2018]   |  

Abstract

Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in least mean square (LMS) regression of traditional neural network NUC methods, which is realized by employing a total variation penalty in the cost function and redesigning the processing architecture. Moreover, an adaptive gated learning rate is presented to further reduce the ghosting artifacts and guarantee fast convergence. The performance of the proposed algorithm is comprehensively investigated with artificially corrupted test sequences and real infrared image sequences, respectively. Experimental results show that the proposed algorithm can effectively accelerate the convergence speed, suppress ghosting artifacts, and promote correction precision. View Full-Text
Keywords: infrared imaging; nonuniformity correction; total variation; neural network infrared imaging; nonuniformity correction; total variation; neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lai, R.; Yue, G.; Zhang, G. Total Variation Based Neural Network Regression for Nonuniformity Correction of Infrared Images. Symmetry 2018, 10, 157.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top