Deep learning-based methods have made significant progress in image dehazing. However, these methods often falter when applied to real-world hazy images, primarily due to the scarcity of paired real-world data and the limitations of current dehazing feature extractors. Toward these issues, we introduce a novel Physics Embedded Illumination Estimation (PEIE) method for adaptive real-world dehazing. Specifically, (1) we identify the limitations of the widely used Atmospheric Scattering Model and propose a new physical model, the Illumination-Adaptive Scattering Model (IASM), for more accurate illumination representation in hazy imaging; (2) we develop a robust data synthesis pipeline that leverages the physics embedded illumination estimation to generate realistic hazy images; and (3) we design an Illumination-Adaptive Dehazing Unit (IDU) to extract dehazing features consistent with our proposed IASM in the latent space. By integrating the IDU into a U-Net architecture to create IADNet, we achieve significant improvements in dehazing performance through end-to-end training on synthetic data. Extensive experiments validate the superior performance of our PEIE method, significantly surpassing the state-of-the-arts in real-world dehazing.

The atmospheric scattering model is one of the most widely used model to describe the optical imaging processing of hazy images. However, the global atmospheric light used in the traditional atmospheric scattering model has limitations in describing images with varying local environmental illumination. In this paper, by extending the global atmospheric light to the local illumination, a non-uniform scattering model is proposed, which can better describe real scenes under non-uniform environmental illumination. Based on this model, an adaptive local illumination estimation for hazy image is proposed, which can adapt to the local differences of environment illumination. The experimental results demonstrate that the proposed algorithm can outperform the state-of-the-art algorithms in terms of not only the non-uniform scattering removal but also the adaptability.