MCRNet: Underwater image enhancement using multi-color space residual network

Abstract

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Keywords

• Underwater image enhancement
• Deep learning
• Color correction
• Underwater robots