GLUE-GAN: Global-local underwater image enhancement generative adversarial network

Underwater images suffer from color cast, blur, and low contrast due to wavelength-dependent absorption, scattering, and suspended particulates. Many enhancement methods restore global appearance while suppressing local features; others operate in the frequency domain or rely on auxiliary regularization but lack an explicit mechanism to fuse global and local evidence. We introduce GLUE-GAN, a generative adversarial network that globally–locally unifies enhancement through explicit cross-scale, cross-domain fusion. The model comprises: (1) an adaptive feature enhanced encoder that marries spatial context modules with grouped channel-wise self-attention to collaboratively model spatial–channel dependencies across diverse scenes; (2) a multichannel feature aggregation enhancement module that performs multi-scale extraction and alignment to uniformly recover global tone and local textures; and (3) a global–local information enhancement module that uses wavelet decomposition to separate low- and high-frequency bands, processing that mitigates local bias during global correction. Evaluations on EUVP, UIEB, and UFO-120 demonstrate consistent gains in color fidelity, contrast, and sharpness, with improved preservation of edges and fine details. By unifying spatial–channel reasoning with frequency-aware processing in a single adversarial framework, GLUE-GAN balances global color correction and local detail preservation for underwater image enhancement.