While deep learning methods show promising visual results, current end-to-end networks often lack tailored architectures to address common issues like color distortion and texture blurriness. To improve their effectiveness, we propose an underwater image enhancement network that utilizes joint texture perception and color histogram features. The network comprises a texture-aware module, a color histogram extraction module, and a color-texture fusion enhancement module. The texture-aware network incorporates a deformable transformer module, leveraging spatially aware deformable convolution to enhance multi-head attention and extract texture features. The color histogram extraction module harnesses histograms from real underwater images to compute the loss function. Subsequently, the color-texture fusion module merges the color and texture features, which are then processed by the enhancement network to produce the final results. This approach effectively preserves texture structures, corrects color distortions, and maintains information consistency. Extensive experiments demonstrate that our method surpasses existing underwater image enhancement algorithms, achieving a 10% increase in the UIQM metric and reducing processing time to just 0.051 s per image. Our model successfully meets the demands of underwater visual enhancement tasks.