Dynamic Structure-Aware Modulation Network for Underwater Image Super-Resolution.

Abstract

Image super-resolution (SR) is a formidable challenge due to the intricacies of the underwater environment such as light absorption, scattering, and color distortion. Plenty of deep learning methods have provided a substantial performance boost for SR. Nevertheless, these methods are not only computationally expensive but also often lack flexibility in adapting to severely degraded image statistics. To counteract these issues, we propose a dynamic structure-aware modulation network (DSMN) for efficient and accurate underwater SR. A Mixed Transformer incorporated a structure-aware Transformer block and multi-head Transformer block, which could comprehensively utilize local structural attributes and global features to enhance the details of underwater image restoration. Then, we devised a dynamic information modulation module (DIMM), which adaptively modulated the output of the Mixed Transformer with appropriate weights based on input statistics to highlight important information. Further, a hybrid-attention fusion module (HAFM) adopted spatial and channel interaction to aggregate more delicate features, facilitating high-quality underwater image reconstruction. Extensive experiments on benchmark datasets revealed that our proposed DSMN surpasses the most renowned SR methods regarding quantitative and qualitative metrics, along with less computational effort.