Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Green Vision: A Smart and Sustainable Image Restoration Pipeline

Authors
Amol Bhosle1, 2, Kailas Patil1, *, Napattarapong Chamchoy3, Prawit Chumchu3
1Vishwakarma University, Pune, India
2MIT Art, Design and Technology University, Pune, India
3Kasetsart University Sriracha Campus, Thung Sukhla, Thailand
*Corresponding author. Email: kailas.patil@vupune.ac.in
Corresponding Author
Kailas Patil
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_14How to use a DOI?
Keywords
Image restoration; sustainability; AI pipelines; degradation detection; adaptive restoration; image enhancement; real-world datasets; energy efficiency
Abstract

In this work, Green Vision picture restoration pipeline proposed which combines final enhancement, adaptive restoration, and deterioration detection into a single modular architecture. The suggested solution maintains scalability through modular architecture while achieving eco-efficient picture restoration by utilizing lightweight, pretrained AI models and open-source components. Both artificial and real-world damaged photos are thoroughly evaluated under a variety of circumstances, including blur, poor light, and ambient noise. PSNR, SSIM, and error heatmaps are used to benchmark performance, and comparative analysis is extended to academic restoration processes and commercial online services. The sustainability claim is further supported by runtime and energy usage figures.

The findings show that the Green Vision pipeline provides an accessible and resource-efficient approach for large-scale implementation, achieving competitive restoration quality with a smaller computing footprint.

Copyright
© 2025 The Author(s)
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 International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_14How to use a DOI?
Copyright
© 2025 The Author(s)
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

risenwbib
TY  - CONF
AU  - Amol Bhosle
AU  - Kailas Patil
AU  - Napattarapong Chamchoy
AU  - Prawit Chumchu
PY  - 2026
DA  - 2026/01/06
TI  - Green Vision: A Smart and Sustainable Image Restoration Pipeline
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 206
EP  - 222
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_14
DO  - 10.2991/978-94-6463-948-3_14
ID  - Bhosle2026
ER  -