  Underwater image processing and analysis have been a hotspot of study in
recent years, as more emphasis has been focused to underwater monitoring and
usage of marine resources. Compared with the open environment, underwater image
encountered with more complicated conditions such as light abortion,
scattering, turbulence, nonuniform illumination and color diffusion. Although
considerable advances and enhancement techniques achieved in resolving these
issues, they treat low-frequency information equally across the entire channel,
which results in limiting the network's representativeness. We propose a deep
learning and feature-attention-based end-to-end network (FA-Net) to solve this
problem. In particular, we propose a Residual Feature Attention Block (RFAB),
containing the channel attention, pixel attention, and residual learning
mechanism with long and short skip connections. RFAB allows the network to
focus on learning high-frequency information while skipping low-frequency
information on multi-hop connections. The channel and pixel attention mechanism
considers each channel's different features and the uneven distribution of haze
over different pixels in the image. The experimental results shows that the
FA-Net propose by us provides higher accuracy, quantitatively and qualitatively
and superiority to previous state-of-the-art methods.
