Underwater image enhancement via LBP-based attention residual network

Abstract

Owing to the influence of light absorption and scattering in underwater environments, underwater images exhibit color deviation, low contrast and detail blur, and other degradations. This paper proposes an underwater image enhancement method combining a residual convolution network, local binary pattern (LBP), and self-attention mechanism. The LBP operator processes the input underwater images. The LBP feature images and underwater images thus obtained constitute the network input. The network consists of three modules: a color correction module to remove the color deviation in underwater images, detail repair module to restore the integrity of details, and an LBP auxiliary enhancement module for global enhancement of image details. The correction and repair modules generate the correct color image and detailed supplement images, respectively. The final-result image is obtained by superpositioning the two generated images. The experimental results confirm that our method can reproduce the bright colors and complete details of the visual effect, showing a significant improvement over other advanced methods in quantitative evaluation.