Underwater images often suffer from degradation due to refraction, back-scattering, and absorption, resulting in color cast, blur, and limited visibility. Such degradation hampers higher-level computer vision applications in autonomous underwater vehicles. Existing methods for enhancing degraded images often fail to preserve fine edges and true colors. Hence, an effective pre-processing network is vital for underwater image enhancement. Addressing this need, we propose a frequency modulated deformable transformer network. Initially, our method extracts features using a multi-scale feature fusion feed-forward module. Further, a frequency modulated deformable attention module reconstructs fine-level textures in the restored image. We introduce a spatio-channel attentive offset extractor in the modulated deformable convolution for focusing on relevant contextual information. Additionally, we propose adaptive edge-preserving skip connections to propagate prominent edge features. Our method outperforms existing state-of-the-art methods, as demonstrated by comprehensive evaluations on synthetic and real-world datasets, and extensive ablation analysis. Testing code is provided in the supplementary material and will be made available upon final acceptance.