  We present a wavelet-based dual-stream network that addresses color cast and
blurry details in underwater images. We handle these artifacts separately by
decomposing an input image into multiple frequency bands using discrete wavelet
transform, which generates the downsampled structure image and detail images.
These sub-band images are used as input to our dual-stream network that
incorporates two sub-networks: the multi-color space fusion network and the
detail enhancement network. The multi-color space fusion network takes the
decomposed structure image as input and estimates the color corrected output by
employing the feature representations from diverse color spaces of the input.
The detail enhancement network addresses the blurriness of the original
underwater image by improving the image details from high-frequency sub-bands.
We validate the proposed method on both real-world and synthetic underwater
datasets and show the effectiveness of our model in color correction and blur
removal with low computational complexity.
