Autonomous underwater robot for underwater image enhancement via multi-scale deformable convolution network with attention mechanism

Abstract

Underwater images suffer from poor visibility quality, which results from selective attenuation and scattering. These interdependence phenomena together cause image degradation, failing autonomous underwater robots to recognize image contents. To address those problems, we propose a multi-scale deformable convolution network with an attention mechanism (MsDCANet) to enhance the quality of underwater images. The proposed model is generally implemented by an encoder-decoder architecture. Concretely, we first propose a multi-scale deformable convolutional network, which acquires deformable local receptive fields at different scales and enriches the diversity of feature representation. Coupled with spatial and channel attention mechanisms, the task-oriented high-level representations acquired from input feature maps, and the most significant features are highlighted. Considering the contributions of the encoder and decoder functions on the final enhancement task, an attention-based skip fusion scheme is designed to improve the original skip connection, which aims to reconstruct higher quality underwater images. Finally, the proposed model is optimized by a multi-task loss function, including pixel loss and perceptual loss. Experiments on underwater images captured under diverse scenes demonstrate that the proposed model produces visually pleasing results and has strong generalization ability, even significantly outperforming several state-of-the-art methods. Besides, our approach can also improve the performance of vision tasks and be applied to aquaculture development.