VLMs Win a Systematic Evaluation of Underwater Image Reconstruction
arXiv:2608.11425v1 Announce Type: new Abstract: Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. This paper describes a systematic evaluation pipeline for underwater reconstruction, which can be used to assess a method for accuracy; consistency of reconstruction over camera moves; and the effect of water parameters. We use this pipeline to evaluate a range of current procedures, from models constructed using explicit but approximate physical models of scattering to Vision-Language Models (VLMs which are not currently trained with explicit physical models). Overall, VLMs wholly and significantly outperform physically based models in our evaluation, likely because of the importance of a strong image prior. Results on images of real underwater scenes strongly confirm the evaluation.
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[Submitted on 11 Aug 2026]
Title:VLMs Win a Systematic Evaluation of Underwater Image Reconstruction
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Abstract:Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. This paper describes a systematic evaluation pipeline for underwater reconstruction, which can be used to assess a method for accuracy; consistency of reconstruction over camera moves; and the effect of water parameters. We use this pipeline to evaluate a range of current procedures, from models constructed using explicit but approximate physical models of scattering to Vision-Language Models (VLMs which are not currently trained with explicit physical models). Overall, VLMs wholly and significantly outperform physically based models in our evaluation, likely because of the importance of a strong image prior. Results on images of real underwater scenes strongly confirm the evaluation.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.11425 [cs.CV]
(or arXiv:2608.11425v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.11425
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sara Aghajanzadeh [view email] [v1] Tue, 11 Aug 2026 20:46:46 UTC (44,190 KB)
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