Underwater image enhancement has drawn a
lot of attention because of its significance in marine
engineering and aquatic robotics. Many techniques for
enhancing underwater photographs have been put forth
in recent years. Since light propagation underwater and
in the atmosphere are different, a particular set of nonlinear visual distortions occur. These distortions are
brought on by a variety of factors. Red wavelengths are
absorbed in deep water as light travels further, which is
why underwater images frequently have a green or blue
colour as the dominating hue. Low-contrast, fuzzy, and
color-degraded images are the result of such
wavelength-dependent attenuation, scattering, and other
optical properties of water bodies that cause irregular
distortions. The previous CNN-GAN (Generative
Adversarial Network) based model for real-time
underwater image enhancement is sped up by the
upgraded inception model proposed by GAN-Based
Underwater Image Enhancement. The suggested model
assesses image quality based on its global colour,
content, local texture, and style information to construct
a perceptual loss function. The dataset being used, called
EUVP (Enhancing Underwater Visual Perception), is
made up of paired and unpaired collections of
underwater images captured by seven distinct cameras
under a variety of visibility conditions during maritime
explorations and cooperative experiments. The
suggested model's accuracy can be increased by learning
to enhance and improve underwater image quality from
both paired and unpaired training
Keywords :
GAN, EUVP, Image enhancement, Underwater images