Marine Snow Removal Using Internally Generated Pseudo Ground Truth

Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow—suspended organic particles appearing as bright spots or noise—significantly impacts machine vision tasks, particularly those involving feature matching. Existing methods for removing marine snow are ineffective due to the lack of paired training data. To address this challenge, this paper proposes a novel enhancement framework that introduces a new approach to generating paired datasets from raw underwater videos. The resulting dataset consists of paired images of generated snowy and snow-free underwater videos, enabling supervised training for video enhancement. We describe the dataset creation process, key characteristics, and demonstrate its effectiveness in enhancing underwater image restoration with the absence of groundtruth.