Dehazing real-world hazy images is challenging due to the complexity of natural haze, varying haze conditions, details preservation, and the risk of overexposure. Existing methods excel in synthetic hazy scenarios but struggle in the real world because they don’t use all available features. Classical dehazing techniques primarily focus on low-level dehazing enhancements, whereas deep learning-based methods extract more intricate weather-related features. However, both of these approaches exhibit limitations in effectively addressing the real-world dehazing. To address these challenges, we introduce an innovative approach that combines the strengths of both modalities to dehaze and enhance the visibility of real-world hazy scenes. Firstly, we extract both low-level and deep features and then employ a pre-trained vector quantization GAN to create well-detailed data patches. A decoder, with a normalized module, effectively utilizes these high-quality features. Additionally, we introduce a controllable operation to improve feature matching. To further enhance dehazing and generalizability, the decoder’s output undergoes a sequence of gamma-correction operations and generates a series of multi-exposure images that are combined to create a haze-free and higher-quality image. Our method effectively reduces haziness, enhances sharpness, preserves natural colors, and minimizes artifacts in challenging real-world scenarios. The approach surpasses five SOTA methods in both qualitative and quantitative evaluations across three key metrics, utilizing three synthetic and two real-world hazy datasets. Notably, it achieves a substantial improvement in real-world datasets over the second-best method, with 0.5702 and 0.129 in FADE metrics for the RTTS and Fattal datasets, respectively.