Underwater Image Enhancement with Phase Transfer and Attention

Underwater pictures typically suffer from substantial deterioration due to the refraction and absorption of light by water, including color cast, hazy blur, and limited visibility. Such degradation in visibility eventually reduces the effectiveness of marine applications installed on autonomous underwater vehicles. Hence, an efficient pre-processing step is required for the significant performance of these applications. As a solution, underwater image enhancement (UIE) mainly focuses on enhancing the visibility of degraded images along with restoring crucial details. Existing methods generally utilize (a) complex cascaded architectures, (b) different degradation-prone color spaces, and (c) direct skip connections that pass irrelevant content. In light of this, we propose a lightweight transformer network with 1.7M parameters (1/6 ^th of the existing state-of-the-art method) consisting of the proposed gray-scale attention and phase transformer block for UIE. A gray-scale attention block is proposed for the effective extraction of non-contaminated features. Further, a phase transfer block is proposed for effectively restoring the structural information in the outputs by propagating most relevant and undegraded features from the inputs. A comprehensive evaluation of the proposed method on synthetic (EUVP, UIEB) and real-world (UIEB, UCCS) image datasets as well as extensive ablation studies confirm its effectiveness over existing state-of-the-art approaches. The source code is provided at: https://github.com/Mdraqibkhan/UIEPTA.