125 research outputs found
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules
Underwater images are fundamental for studying and understanding the status
of marine life. We focus on reducing the memory space required for image
storage while the memory space consumption in the collecting phase limits the
time lasting of this phase leading to the need for more image collection
campaigns. We present a novel machine-learning model that reconstructs the
colours of underwater images from their luminescence channel, thus saving 2/3
of the available storage space. Our model specialises in underwater colour
reconstruction and consists of an encoder-decoder architecture. The encoder is
composed of a convolutional encoder and a parallel specialised classifier
trained with webly-supervised data. The encoder and the decoder use layers of
capsules to capture the features of the entities in the image. The colour
reconstruction process recalls the progressive and the generative adversarial
training procedures. The progressive training gives the ground for a generative
adversarial routine focused on the refining of colours giving the image bright
and saturated colours which bring the image back to life. We validate the model
both qualitatively and quantitatively on four benchmark datasets. This is the
first attempt at colour reconstruction in greyscale underwater images.
Extensive results on four benchmark datasets demonstrate that our solution
outperforms state-of-the-art (SOTA) solutions. We also demonstrate that the
generated colourisation enhances the quality of images compared to enhancement
models at the SOTA
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