A PDE-Based Image Dehazing Method via Atmospheric Scattering Theory

Abstract

This paper presents a novel partial differential equation (PDE) framework for single-image dehazing. By integrating the atmospheric scattering model with nonlocal regularization and dark channel prior, we propose the improved PDE: \[ -div (D( u) u ) + (t) G(u) = (I,t,A) \] where D( u) = (| u| + )^-1 is the edge-preserving diffusion coefficient, G(u) is the Gaussian convolution operator, and (t) is the adaptive regularization parameter based on transmission map t. We prove the existence and uniqueness of weak solutions in H_0^1() using Lax-Milgram theorem, and implement an efficient fixed-point iteration scheme accelerated by PyTorch GPU computation. The experimental results demonstrate that this method is a promising deghazing solution that can be generalized to the deep model paradigm.

Tasks

Reproductions