Underwater Color Correction Network With Knowledge Transfer

Underwater images suffer from severe color distortion, due to the wavelength-dependent light attenuation and scattering. Various underwater image enhancement methods have been developed to improve the quality of degraded underwater images. However, contemporary approaches often overlook the impact of different scene colors on the overall process, potentially leading to undesired outcomes, such as enhanced images exhibiting excessive redness. In this paper, we observe that the color tones of degraded underwater images exhibit variability under the influence of different underwater targets and scenes. Each degraded color channel can be utilized to guide the color correction of other channels. Given this, a light-weight underwater color correction network, dubbed UCCNet, is presented to alleviate the issue of color corruption. In UCCNet, three parallel branches are designed to excavate the residual information within each color channel, subsequently leveraging these features to improve the quality of underwater images. Moreover, facing the challenge of effectively enhancing underwater images in diverse and complex scenes, the model UCCNet-KT is established based on UCCNet. In UCCNet-KT, the technology of knowledge transfer is designed to improve the generalization ability by enriching the dataset and constructing the loss function. Extensive experiments on various underwater datasets indicate the impressive performance of the UCCNet and UCCNet-KT qualitatively and quantitatively.