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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 2×, 3×, or 4× 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 visuallyguided underwater robots. The model and dataset information will be available at: https://github.com/ xahidbuffon/Deep-SESR.

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A AB BS ST TR RA AC CT T Finite element method (FEM) suffers from a serious mesh distortion problem when used for high velocity impact analyses. The smooth particle hydrodynamics (SPH) method is appropriate for this class of problems involving severe damages but at considerable computational cost. It is beneficial if the latter is adopted only in severely distorted regions and FEM further away. The coupled smooth particle hydrodynamics -finite element method (SFM) has been adopted in a commercial hydrocode LS-DYNA to study the perforation of Weldox 460E steel and AA5083-H116 aluminum plates with varying thicknesses and various projectile nose geometries including blunt, conical and ogival noses. Effects of the SPH domain size and particle density are studied considering the friction effect between the projectile and the target materials. The simulated residual velocities and the ballistic limit velocities from the SFM agree well with the published experimental data.The study shows that SFM is able to emulate the same failure mechanisms of the steel and aluminum plates as observed in various experimental investigations for initial impact velocity of 170 m/s and higher.

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