P3C-DNet: Pseudo-Groundtruth Contrastive Learning With Color Calibration Dehazing Network

Existing dehazing methods primarily rely on synthetic hazy images for supervised learning. While effective on synthetic datasets, these methods often struggle to generalize to real-world hazy images, leading to issues such as color distortion and incomplete haze removal. Moreover, their limited adaptability to real-world datasets and inability to handle complex haze scenarios remain significant challenges. To address these limitations, we propose a novel unsupervised framework P3C-DNet (Pseudo-groundtruth Contrastive learning with Color Calibration Dehazing Network). Our P3C-DNet introduces a Pseudo-groundtruth image generation strategy through the Pseudo-groundtruth Contrastive Supervision (PCS) module, which overcomes the lack of real haze-free training data by generating high-quality Pseudo-groundtruth images. To further refine the dehazing process, we incorporate a codebook-based image coding and matching mechanism that aligns Pseudo-groundtruth images with hazy inputs, enhancing the accuracy and detail of the dehazed outputs. To address the prevalent issue of color distortion, especially in complex environments, our P3C-DNet integrates a Dynamic Color Restoration Block (DCRB) to ensure visual quality and color consistency in the dehazed results. Experimental evaluations demonstrate that our P3C-DNet achieves superior performance in haze removal, color fidelity, and detail preservation, significantly outperforming existing methods and setting a new benchmark for real-world dehazing tasks.