Despite significant progress in real-world image dehazing, efficiently generating high-fidelity, haze-free videos (especially in driving scenarios) remains challenging. Existing methods generally extend image dehazing techniques to videos by employing pre-trained single image dehazing models for preprocessing followed by refinement stages. However, this disjointed two-stage process often leads to unrealistic textures and loss of detail, as it fails to leverage large amounts of high-quality images for prior learning and the subsequent refinement struggles to correct temporal inconsistencies across frames introduced in the first stage. To address these issues, we propose DVDPEC: a Driving Video Dehazing framework utilizing a Position Embedding-based (PE-based) Codebook and a novel Flow Selective Block (FSB). The PE-based codebook stores fine-grained, spatially aware textural information specific to driving videos and leverages implicit positional embeddings for precise, position-aware codebook matching. This enables accurate prior retrieval and improves dehazing results. The FSB aggregates information from adjacent frames by dynamically combining both image flow and prior flow, effectively mitigating flow estimation ambiguities caused by haze. It enhances information fusion across frames, leading to more coherent and visually appealing dehazed videos. Extensive experiments demonstrate that DVDPEC achieves state-of-the-art performance on real-world driving video dehazing tasks, significantly enhancing texture preservation and visual fidelity.

Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural features from hazy RGB images and the complexity of acquiring real haze/clean image pairs outside controlled environments like smoke-filled scenes. To address these issues, we first propose a novel Structure Guided Dehazing Network (SGDN) that leverages the superior structural properties of YCbCr features over RGB. It comprises two key modules: Bi-Color Guidance Bridge (BGB) and Color Enhancement Module (CEM). BGB integrates a phase integration module and an interactive attention module, utilizing the rich texture features of the YCbCr space to guide the RGB space, thereby recovering clearer features in both frequency and spatial domains. To maintain tonal consistency, CEM further enhances the color perception of RGB features by aggregating YCbCr channel information. Furthermore, for effective supervised learning, we introduce a Real-World Well-Aligned Haze dataset, which includes a diverse range of scenes from various geographical regions and climate conditions. Experimental results demonstrate that our method surpasses existing state-of-the-art methods across multiple real-world smoke/haze datasets.

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C2PNet. Extensive experiments demonstrate that our C2PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. Code is available at https://github.com/YuZheng9/C2PNet.