Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

• URL: http://arxiv.org/abs/2606.25732v1
• Date: Wed, 24 Jun 2026 11:58:29 GMT
• Title: Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling
• Abstract summary: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering.<n>We propose PGL-Net, a lightweight framework that incorporates physical inductive biases via operator-level emulation.<n>PGL-Net achieves state-of-the-art restoration quality with significantly reduced complexity.
• Score: 9.594040041748388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight and low-latency models. Existing approaches either rely on fragile physical inversion under simplified assumptions or adopt heavy blind architectures unsuitable for edge deployment. To overcome these limitations, we propose PGL-Net (Physics-Inspired Global-Local Decoupling Network), a lightweight framework that incorporates physical inductive biases via operator-level emulation, avoiding explicit parameter estimation. It decouples dehazing into global distribution rectification and local structural refinement. A Physics-Inspired Affine Fusion (PAF) module performs globally conditioned alignment across hierarchical skip connections to compensate for haze-induced bias, while a compact Degradation-Aware Modulation (DAM) block adaptively restores spatially and spectrally variant details through dynamic feature modulation. Extensive experiments on multiple real-world benchmarks demonstrate that PGL-Net achieves state-of-the-art restoration quality with significantly reduced complexity. Compared with the recent SOTA SGDN, the Tiny variant (PGL-Net-T) improves PSNR by up to 2.6dB and consistently enhances downstream object detection accuracy, while achieving over a 10x reduction in inference latency. Code is publicly available at: https://github.com/sc-30-bit/PGL-Net.

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