Underwater image super-resolution using deep residual multipliers [conference paper]

Conference

IEEE International Conference on Robotics and Automation (ICRA) - May 31, 2020

Authors

Md Jahidul Islam (Ph.D. student), Sadman Sakib Enan (Ph.D student), Peigen Luo (undergraduate research assistant), Junaed Sattar (assistant professor)

Abstract

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of `high' (640×480) and `low' (80 × 60, 160 × 120, and 320×240) resolution. USR-248 contains paired instances for supervised training of 2×, 4×, or 8× SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models' performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions.

Link to full paper

Underwater image super-resolution using deep residual multipliers

Keywords

underwater robotics