Comparative Study of Low-Light Image Enhancement in Additive Manufacturing

Deepika Nikam; Sindhuja Mullangi; Tejaswini Bhosale; Shiva Shekar; Sagar Nikam

doi:10.2991/978-94-6239-813-9_24

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Comparative Study of Low-Light Image Enhancement in Additive Manufacturing

Authors

Deepika Nikam¹, Sindhuja Mullangi¹, Tejaswini Bhosale²^{, *}, Shiva Shekar³, Sagar Nikam¹

¹School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Northland Road, Derry/Londonderry, BT48 7JL, Northern Ireland, UK

²MIT Art Design & Technology University, Pune, India

³Laboratory for Advanced Manufacturing and Processing, Indian Institute of Technology Jammu, Jammu, 181221, India

^*Corresponding author. Email: tejaswini.bhosale@mituniversity.edu.in

Corresponding Author

Tejaswini Bhosale

Available Online 29 October 2026.

DOI: 10.2991/978-94-6239-813-9_24 How to use a DOI?
Keywords: WAAM; low-light enhancement; Image processing
ABSTRACT: The images captured using the Wire Arc Additive Manufacturing (WAAM) process may exhibit problems such as bright arc glare, sensor noise, and non-uniform illumination of the image which can hinder the analysis of bead geometry and heat-affected-zone (HAZ) features. In the proposed research to enhance the image quality, five techniques have been tested: CLAHE, Multi-Scale Retinex, LIME-lite, KinD-style Gamma and DCE-lite. The automated enhancement pipeline has been used to evaluate them. The enhanced images are evaluated by PSNR, SSIM, Tenengrad, Shannon entropy and bright-fraction analysis to identify the performance of the proposed image enhancement techniques. Obtained results revealed that the overall assessment increases the contrast, structural preservation, sharpness and detail while reducing the saturation of highlights. This increases visibility of manufacturing features and potential defects, complementing the ability of the WAAM to be monitored with greater confidence while using relatively low computational resources.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Global Symposium on Adaptive Manufacturing (GSAM 2026)
Series: Advances in Engineering Research
Publication Date: 29 October 2026
ISBN: 978-94-6239-813-9
ISSN: 2352-5401
DOI: 10.2991/978-94-6239-813-9_24 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Deepika Nikam
AU  - Sindhuja Mullangi
AU  - Tejaswini Bhosale
AU  - Shiva Shekar
AU  - Sagar Nikam
PY  - 2026
DA  - 2026/10/29
TI  - Comparative Study of Low-Light Image Enhancement in Additive Manufacturing
BT  - Proceedings of the  Global Symposium on Adaptive Manufacturing (GSAM 2026)
PB  - Atlantis Press
SP  - 70
EP  - 72
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-813-9_24
DO  - 10.2991/978-94-6239-813-9_24
ID  - Nikam2026
ER  -

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