Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Year: 2020
Type: conference-paper
Abstract: In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications.We present Deep SESR, a residualin-residual network-based generative model that can learn to restore perceptual image qualities at 2×, 3×, or 4× higher spatial resolution.We supervise its training by formulating a multi-modal objective function that addresses the chrominancespecific underwater color degradation, lack of image sharpness, and loss in high-level feature representation.It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement.We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference.Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples.By thorough experimental evaluation on UFO-120 and several other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution.We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects.Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots.
Source: Robotics: Science and Systems
Authors: Md Jahidul Islam, Peigen Luo, Junaed Sattar
Institution: University of Minnesota
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: Nvidia, University of Minnesota
OpenAlex ID: W3004469096