PAUNet: A Lightweight Network for Real-Time Underwater Image Enhancement

Underwater images are degraded by wavelength-dependent absorption and scattering, while enhancement on resource-constrained platforms requires a small model and low inference latency. We propose PAUNet, a lightweight encoder–decoder network combining partial axial depthwise convolution (PADW), multiscale axial spatial adaptive attention (MASA), and attention-guided skip connections. PADW filters half of the feature channels and preserves the remainder through an identity branch. MASA combines three axial scales using spatial branch attention. A joint objective constrains reconstruction, structure, perceptual features, and color. PAUNet achieves PSNR values of 21.508, 28.696, and 28.632 dB on UIEB, EUVP, and LSUI, respectively, with SSIM values of 0.751, 0.843, and 0.895. Among the compared methods, PAUNet has the highest PSNR on EUVP and LSUI, ranks second in UIEB PSNR, and achieves the highest LSUI SSIM. PAUNet contains 64,495 parameters, requires 0.493 GFLOPs, and runs at 288.95 frames per second on an NVIDIA RTX 3090. On an NVIDIA Jetson Xavier NX, TensorRT FP16 inference reaches 74.55 frames per second at 256 × 256 resolution and batch size one. The comparisons and ablations evaluate this compact architecture for underwater image enhancement.