A region-adaptive hybrid framework for single image dehazing using channel priors and retinex analysis
Haze degrades image clarity, contrast, and fine details, causing perceptual ambiguity. Image dehazing aims to restore visibility, but remains challenging in noisy and complex environments. To address these challenges, prior-based dehazing techniques have been widely explored for effective transmission estimation and scene radiance recovery. The Dark Channel Prior (DCP) uses the minimum intensity across all color channels to estimate transmission in dense haze regions efficiently. This approach works well in non-sky regions with rich textures and darker intensity. However, DCP may not work well in sky regions. On the other hand, the Bright Channel Prior (BCP) uses high-intensity information of pixels to achieve efficient dehazing in bright regions, such as light-dominant regions in the atmosphere, which covers the sky. However, BCP may not work well in non-sky regions. Most existing dehazing algorithms use either DCP or BCP to efficiently recover scene radiance. This results in reduced performance in different regions. To overcome this limitation, a region-based fusion approach is developed to achieve efficient scene radiance recovery from DCP and BCP. The fused image is enhanced using a Retinex-based image decomposition technique, which consists of gamma correction of illumination images and wavelet-based denoising of reflectance images. The performance of dehazing depends on the tuning parameters of the above-mentioned techniques. Also, the ground truth may not be available in real-time scenarios. Therefore, a no-reference (blind) adaptive parameter selection scheme is developed to handle the absence of ground truth in real-world images. The most suitable parameters for DCP and BCP are determined using a grid search guided by the NIQE metric, ensuring improved perceptual quality without requiring reference images. The method is evaluated on I-Haze, O-Haze, Dense-Haze, NH-Haze, and Reside (Reside-6K, RTTS, and UAHI) datasets using SSIM, PSNR, NIQE, and BRISQUE metrics. Experimental results demonstrate consistent improvement across diverse haze conditions. Moreover, the proposed framework operates without any training requirements, removing the necessity for annotated datasets and model optimization processes, all while ensuring low computational complexity, with an average processing time per image is just 0.423 s. Furthermore, the proposed approach demonstrates comparable performance to state-of-the-art methods and is validated through real-world object detection tasks. The source code of the proposed method is publicly available at: RegionBasedAdaptiveHybridImageDehazing.