Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process

TL;DR

This paper introduces GeoDiff-LF, a diffusion-based framework that leverages light field geometry to enhance underwater images, significantly reducing color distortion and improving visual quality.

Contribution

It presents a novel diffusion model tailored for underwater light field images, incorporating geometric cues and an optimized sampling strategy for superior enhancement.

Findings

Outperforms existing methods in visual fidelity

Effectively reduces underwater color distortion

Demonstrates superior quantitative performance

Abstract

This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging by leveraging its spatial-angular structure. GeoDiff-LF consists of three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency. By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes. Extensive experiments demonstrate that our framework outperforms existing methods across both visual fidelity and quantitative performance, advancing the state-of-the-art in enhancing underwater imaging. The code will be publicly available at https://github.com/linlos1234/GeoDiff-LF.