Evaluation of a Technique to Simulate LiDAR Image Datasets for Training a Machine Learning-Based Image Enhancement Algorithm

Acquiring field data for machine learning-based image enhancement techniques can be challenging. These techniques often use a supervised learning architecture that requires pairs of degraded and target images that would be unfeasible to acquire in the field. One alternative approach is to employ simulation models that can accurately capture the unique characteristics of a degraded visual environment (DVE). The fidelity or accuracy of these models determines the effectiveness of the fully trained image enhancement algorithm. This paper explores the benefits and deficits of utilizing simulation software to properly portray underwater LiDAR capture in DVEs. This is accomplished by employing 3DSMAX Studios to generate 3D renderings of underwater targets. The Image Systems Engineering Toolbox for Cameras (ISETCAM) is then used to synthetically generate a training dataset. Subjective and objective metrics are devised to measure the effectiveness of these approaches in training a GAN-based underwater LiDAR image enhancement algorithm.