NeuroPump: Simultaneous Geometric and Color Rectification for Underwater Images
This addresses the challenge of restoring underwater images for applications like marine research or photography, offering a novel approach to handle both distortions at once, though it is incremental in combining existing techniques.
The paper tackles the problem of underwater image restoration by simultaneously rectifying geometric and color distortions, proposing NeuroPump, a self-supervised method that outperforms baselines quantitatively and qualitatively.
Underwater image restoration aims to remove geometric and color distortions due to water refraction, absorption and scattering. Previous studies focus on restoring either color or the geometry, but to our best knowledge, not both. However, in practice it may be cumbersome to address the two rectifications one-by-one. In this paper, we propose NeuroPump, a self-supervised method to simultaneously optimize and rectify underwater geometry and color as if water were pumped out. The key idea is to explicitly model refraction, absorption and scattering in Neural Radiance Field (NeRF) pipeline, such that it not only performs simultaneous geometric and color rectification, but also enables to synthesize novel views and optical effects by controlling the decoupled parameters. In addition, to address issue of lack of real paired ground truth images, we propose an underwater 360 benchmark dataset that has real paired (i.e., with and without water) images. Our method clearly outperforms other baselines both quantitatively and qualitatively. Our project page is available at: https://ygswu.github.io/NeuroPump.github.io/.