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One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel\u2010to\u2010pixel networks model for underwater image enhancement by designing an encoding\u2013decoding framework. It employs the convolution layers as encoding to filter the noise, while uses deconvolution layers as decoding to recover the missing details and refine the image pixel by pixel. Moreover, skip connection is introduced in the networks model in order to avoid low\u2010level features losing while accelerating the training process. The model achieves the image enhancement in a self\u2010adaptive data\u2010driven way rather than considering the physical environment. Several comparison experiments are carried out with different datasets. 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