A PDE-Based Image Dehazing Method via Atmospheric Scattering Theory
This provides a principled, mathematically rigorous alternative to data-driven methods for image dehazing, which is useful in computer vision applications like autonomous driving and surveillance.
The paper tackles single-image dehazing by developing a PDE framework that embeds atmospheric scattering theory with edge-preserving diffusion and a nonlocal operator, achieving effective haze removal while preserving image fidelity.
This paper introduces a novel partial differential equation (PDE) framework for single-image dehazing. We embed the atmospheric scattering model into a PDE featuring edge-preserving diffusion and a nonlocal operator to maintain both local details and global structures. A key innovation is an adaptive regularization mechanism guided by the dark channel prior, which adjusts smoothing strength based on haze density. The framework's mathematical well-posedness is rigorously established by proving the existence and uniqueness of its weak solution in $H_0^1(Ω)$. An efficient, GPU-accelerated fixed-point solver is used for implementation. Experiments confirm our method achieves effective haze removal while preserving high image fidelity, offering a principled alternative to purely data-driven techniques.