324 research outputs found
Underwater Image Super-Resolution using Deep Residual Multipliers
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 \textit{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' (640x480) and 'low' (80x60, 160x120, and 320x240) spatial resolution.
USR-248 contains paired instances for supervised training of 2x, 4x, or 8x 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
Cast-Gan: Learning to Remove Colour Cast from Underwater Images
Underwater images are degraded by blur and colour cast caused by the attenuation of light in water. To remove the colour cast with neural networks, images of the scene taken under white illumination are needed as reference for training, but are generally unavailable. As an alternative, one can use surrogate reference images taken close to the water surface or degraded images synthesised from reference datasets. However, the former still suffer from colour cast and the latter generally have limited colour diversity. To address these problems, we exploit open data and typical colour distributions of objects to create a synthetic image dataset that reflects degradations naturally occurring in underwater photography. We use this dataset to train Cast-GAN, a Generative Adversarial Network whose loss function includes terms that eliminate artefacts that are typical of underwater images enhanced with neural networks. We compare the enhancement results of Cast-GAN with four state-of-the-art methods and validate the cast removal with a subjective evaluation
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