Underwater Light Field Retention : Neural Rendering for Underwater Imaging

TL;DR

This paper introduces UWNR, a neural rendering method that learns underwater light fields from real images to generate diverse realistic underwater scenes from a single clean image, improving visual quality and metrics.

Contribution

The paper presents a novel neural rendering approach that adaptively learns underwater light fields directly from authentic images, enabling diverse scene synthesis without hand-crafted models.

Findings

UWNR outperforms previous methods in visual quality and metrics.

It can generate diverse underwater images from a single clean image.

The approach facilitates creating a large underwater dataset with various water qualities.

Abstract

Underwater Image Rendering aims to generate a true-tolife underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics ofthe underwater scene. Thus, it is able to synthesize a wide variety ofunderwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https: //github.com/Ephemeral182/UWNR.