FreeDehaze: Towards Training-Free Real-World Image Dehazing via Diffusion Degradation Prior

Restoring high-quality images from degraded hazy images is a challenging task, particularly in real-world scenarios. Recent investigations seek to address this limitation by exploring advanced methods for synthesizing haze and incorporating real-world hazy images. Due to the inherent diversity and complexity of real-world haze, these methods struggle to accurately model haze representations. Based on our observation that the hazy images generated by advanced text-to-image diffusion models exhibit a remarkable resemblance to real-world haze, it suggests that these diffusion models effectively internalize haze representations. Hence, we propose FreeDehaze, a novel training-free diffusion method for real-world image dehazing. FreeDehaze is a posterior-based framework capable of addressing non-linear dehazing challenges without relying on additional degradation estimation networks. It follows the human cognition for image restoration, beginning with perception and subsequently enhancing the image. The core method initially generates pseudo-clean images based on abstract textual descriptions. Subsequently, optimal transport aligns the denoising network output with the pseudo-clean image within a PCA-based haze subspace, facilitating high-fidelity dehazing. Extensive experiments demonstrate that FreeDehaze outperforms comparative methods in subjective metrics on challenging datasets (e.g., RTTS, URHI, and O-HAZE) and achieves competitive objective metrics, demonstrating strong generalization although without additional training.