Real-time Model-based Image Color Correction for Underwater Robots

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

MODELLING COLOUR ABSORPTION OF UNDERWATER IMAGES USING SFM-MVS GENERATED DEPTH MAPS

Abstract. The problem of colour correction of underwater images concerns not only surveyors, who primarily use images for photogrammetric purposes, but also archaeologists, marine biologists, and many other domains experts whose aim is to study objects and lifeforms underwater. Different methods exist in the literature; some of them provide outstanding results but works involving physical models that take into account additional information and variables (light conditions, depths, camera to objects distances, water properties) that are not always available or can be measured using expensive equipment or calculated using more complicated models. Some other methods have the advantages of working with basically all kinds of dataset, but without considering any geometric information, therefore applying corrections that work only in very generic conditions that most of the time differs from the real-world applications.This paper presents an easy and fast method for restoring the colour information on images captured underwater. The compelling idea is to model light backscattering and absorption variation according to the distance of the surveyed object. This information is always obtainable in photogrammetric datasets, as the model utilises the scene's 3D geometry by creating and using SfM-MVS generated depth maps, which are crucial for implementing the proposed methodology. The results presented visually and quantitatively are promising since they are an excellent compromise to provide a straightforward and easily adaptable workflow to restore the colour information in underwater images

Quantitative Evaluation of Color Enhancement Methods for Underwater Photogrammetry in Very Shallow Water: a Case Study

Underwater photogrammetry is often hampered by chromatic aberration, leading to degraded 2D and 3D products. This study investigates the effectiveness of various color enhancement methods in addressing these challenges.Theoretical considerations indicate that light penetration depth varies inversely with wavelength, causing underwater images to exhibit a blue or green cast with increasing depth. Color enhancement techniques can restore natural colors by compensating for this spectral attenuation. Additionally, scattering, caused by light reflected by particles in the water, can introduce haze into underwater images. Color enhancement can mitigate scatter and improve image clarity. In this contribution, to quantitatively evaluate color enhancement methods, we compare original images with images processed using gray-world assumption methods and physical methods that account for the physical properties of light underwater. Using artificial intelligence (AI) for underwater image color enhancement, a data-driven approach was also employed. These methods were applied to a case study concerning a Roman Navis Lapidaria shipwreck carrying five monumental cipollino marble columns at a depth of 4.5 meters in the Porto Cesareo Marine Protected Area (Italy). These methods were compared quantitatively and qualitatively, and the results are presented and discussed