SUCRe: Leveraging Scene Structure for Underwater Color Restoration (2212.09129v3)

Abstract: Underwater images are altered by the physical characteristics of the medium through which light rays pass before reaching the optical sensor. Scattering and wavelength-dependent absorption significantly modify the captured colors depending on the distance of observed elements to the image plane. In this paper, we aim to recover an image of the scene as if the water had no effect on light propagation. We introduce SUCRe, a novel method that exploits the scene's 3D structure for underwater color restoration. By following points in multiple images and tracking their intensities at different distances to the sensor, we constrain the optimization of the parameters in an underwater image formation model and retrieve unattenuated pixel intensities. We conduct extensive quantitative and qualitative analyses of our approach in a variety of scenarios ranging from natural light to deep-sea environments using three underwater datasets acquired from real-world scenarios and one synthetic dataset. We also compare the performance of the proposed approach with that of a wide range of existing state-of-the-art methods. The results demonstrate a consistent benefit of exploiting multiple views across a spectrum of objective metrics. Our code is publicly available at https://github.com/clementinboittiaux/sucre.

• The paper introduces SUCRe, a novel method that leverages multi-view scene structure to tackle underwater color distortions.
• It employs Structure-from-Motion and least squares optimization to accurately estimate parameters in an adapted image formation model.
• Empirical results show that SUCRe outperforms traditional methods with superior PSNR and SSIM metrics on both synthetic and real-world datasets.

An Expert Overview of SUCRe: Leveraging Scene Structure for Underwater Color Restoration

The research paper proposes SUCRe, a sophisticated method for underwater image restoration that leverages multi-view scene structure to eliminate the distortions caused by water on light propagation. The SUCRe approach aims to restore the image as if the underwater effects—primarily scattering and wavelength-dependent absorption—were absent, transforming the image to reveal the original colors and details typical of non-aquatic scenes.

Core Contributions

SUCRe's uniqueness lies in exploiting multiple observational perspectives to create a well-posed problem for optimizing an underwater image formation model (UIFM). This multi-view method contrasts with traditional single-image approaches, which often face inherent ambiguities due to limited data availability. The authors utilize Structure-from-Motion (SfM) to enhance estimation accuracy of physical parameters related to underwater imaging, thus generating restored images with a more faithful representation of the original scene's color and texture.

Methodological Insights

Key to SUCRe is an innovative multi-view framework that tracks pixel intensities across various images using SfM techniques. Parameters for an adapted image formation model are derived from these observations, accounting for varying sensor distances, which refines the model's capacity to produce illumination effects akin to an unobstructed view. The derivation of the backscatter and attenuation coefficients is significantly improved through this approach, bypassing the need for exhaustive assumptions necessary in single-image models.

To accomplish the restoration, SUCRe implements a least squares optimization of the UIFM parameters across multiple images. This process is iteratively refined using a gradient descent method, optimizing against a comprehensive set of pairing observations. Such an approach allows for the effective recovery of color information attenuated in low-contrast areas, typically challenging for methods employing a single perspective.

Performance and Benchmarking

Empirical validation of SUCRe's effectiveness involves extensive testing on synthetic datasets and real-world underwater survey data. The results indicate that SUCRe outperforms leading methodologies in the restoration quality as quantified by metrics like PSNR and SSIM on reference datasets such as Varos and Sea-thru D5. Additionally, SUCRe consistently exhibits less variance in error metrics regardless of sensor distance, underscoring the robustness facilitated by its multi-view exploitation.

Implications and Future Directions

The implications of SUCRe extend beyond just enhanced visual clarity for biologically and ecologically significant subsea photography. This research could facilitate new training sets for deep learning models, potentially improving single-image approaches by leveraging SUCRe-processed images as more reliable reference standards.

The SUCRe methodology opens several avenues for future research. Notably, while the paper addresses specific limitations like the consistent water properties assumption, further studies could develop dynamic models to handle temporally or spatially varying underwater conditions, enhancing the model's ecological validity. Moreover, augmenting the approach with deep learning could potentially automate and speed up the restoration process, applying learned models to predict parameters and refine outputs.

In conclusion, SUCRe delivers significant advancements in underwater image restoration by leveraging technological advancements in multi-view geometry and image modeling. As such, it holds promising potential both for enhancing practical underwater exploration and for advancing theoretical understanding of light-abating phenomena.