Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method

TL;DR

This paper introduces a novel 4-D light field dataset and a progressive enhancement framework to improve underwater imaging quality by leveraging geometric cues and depth estimation, outperforming traditional methods.

Contribution

It presents the first 4-D light field underwater image dataset and a new iterative method that combines explicit and implicit depth cues for image enhancement and depth estimation.

Findings

Outperforms traditional 2-D RGB methods in underwater imaging quality.

Effectively corrects color bias in underwater images.

Achieves state-of-the-art performance on the new dataset.

Abstract

In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is anticipated to effectively address the challenges associated with underwater imaging. By leveraging both explicit and implicit depth cues present in 4-D LF images, we propose a progressive, mutually reinforcing framework for underwater 4-D LF image enhancement and depth estimation. Specifically, our framework explicitly utilizes estimated depth information alongside implicit depth-related dynamic convolutional kernels to modulate output features. The entire framework decomposes this complex task, iteratively optimizing the enhanced image and depth information to progressively achieve optimal enhancement results. More importantly, we construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods, comprising 75 underwater scenes and 3675 high-resolution 2K pairs. To craft vibrant and varied underwater scenes, we build underwater environments with various objects and adopt several types of degradation. Through extensive experimentation, we showcase the potential and superiority of 4-D LF-based underwater imaging vis-a-vis traditional 2-D RGB-based approaches. Moreover, our method effectively corrects color bias and achieves state-of-the-art performance. The dataset and code will be publicly available at https://github.com/linlos1234/LFUIE.