  We present a novel underwater image enhancement method termed SCNet to
improve the image quality meanwhile cope with the degradation diversity caused
by the water. SCNet is based on normalization schemes across both spatial and
channel dimensions with the key idea of learning water type desensitized
features. Specifically, we apply whitening to de-correlate activations across
spatial dimensions for each instance in a mini-batch. We also eliminate
channel-wise correlation by standardizing and re-injecting the first two
moments of the activations across channels. The normalization schemes of
spatial and channel dimensions are performed at each scale of the U-Net to
obtain multi-scale representations. With such water type irrelevant encodings,
the decoder can easily reconstruct the clean signal and be unaffected by the
distortion types. Experimental results on two real-world underwater image
datasets show that our approach can successfully enhance images with diverse
water types, and achieves competitive performance in visual quality
improvement.
