  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
residual-in-residual network-based generative model that can learn to restore
perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We
supervise its training by formulating a multi-modal objective function that
addresses the chrominance-specific 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 the UFO-120 and 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. The model and dataset information will be
available at: https://github.com/xahidbuffon/Deep-SESR.
