T-PhISH-Net: temporally consistent underwater image enhancement via a transformer-based extension of PhISH-Net

Underwater video data routinely suffer from color distortion, low contrast, and frame-to-frame flicker due to wavelength-dependent absorption and scattering. Conventional single-frame UIE methods enhance individual images but introduce temporal artifacts when applied to video. We present T-PhISH-Net, a temporally consistent underwater enhancement model that augments the physics-aware PhISH-Net with a causal transformer-based temporal encoder, depth-guided feature fusion, and adaptive loss weighting. A monocular depth estimate (MiDaS DPT-Large) conditions the bilateral illumination grid via FiLM modulation and window-attention gating, improving backscatter–illumination separation in a physically motivated way. Extensive evaluations on Brackish, MVK, UOT32, and VDD-C show strong temporal stability (tSSIM, tPSNR) competitive with state-of-the-art methods, while maintaining competitive per-frame quality (UIQM). Ablations confirm the joint importance of causal attention, depth conditioning, and learned loss weighting. Limitations include dependence on monocular depth quality, added computational overhead from the MiDaS forward pass, and dataset-specific variation in which metric dimension leads. The method requires no optical flow and supports online inference, making it practical for AUV/ROV pipelines and long-term marine monitoring.