Efficient-USR: Prompt Guided Dual-Domain Feature Information for Efficient Underwater Image Super-Resolution

Abstract

Recent advances in deep learning have significantly improved underwater image super-resolution (UISR) performance. However, their large and complex architectures result in huge computational complexity, making them unsuitable for low-power devices such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). In addition, current research emphasizes designing deep models to enhance performance but overlooks the potential benefits of integrating frequency domain information. To overcome these challenges, we propose Efficient-USR, an effective dual-domain information-based lightweight framework for UISR. In Efficient-USR, we analyze the image in different scales by incorporating two core components: a) spatial-frequency interaction block (SFIB) to capture both the global and local context by operating spatial-channel cross-attention on spatial-frequency features and b) prompt-guided cross-attention block (PCAB) to efficiently encode degradation information in prompt component and perform feature fusion of multiple scales via cross-attention. Extensive experiments on two UISR benchmarks demonstrate the effectiveness of our Efficient-USR (e.g., ∼ 1.35% and ∼ 1.40% SSIM improvement on UFO-120 and USR-248 4 datasets, respectively, with only 0.18 M parameters). The code× is available at: https: //github.com/Alik033/Efficient-USR.