VLMs Win a Systematic Evaluation of Underwater Image Reconstruction
Sara Aghajanzadeh, Yingxue Wang, Ieva Bagdonaviciute, David Forsyth
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.