A Variation-Based Nighttime Image Dehazing Flow With a Physically Valid Illumination Estimator and a Luminance-Guided Coloring Model

Image quality is often reduced in hazy weather, especially during the nighttime when image visibility can be further degraded. In this paper, we propose a robust variation-based nighttime image dehazing flow with a physically valid illumination estimator, a luminance-guided coloring model, and a transmission refinement procedure to effectively address this problem. We design a new illumination model to better address the non-global air-light issue in nighttime scenes. Then, we introduce a structure-preserving optimization flow based on Retinex theory to obtain ambient illumination. Color consistency is guaranteed because we use the input image as the initial guess of illumination in our coloring model. A variational procedure is developed to smoothen the estimated transmission map, where the block effect and the halos can be eliminated through the procedure. The proposed luminance-based correction mechanism further improves visual image quality in the presence of a large sky region. Our experiments are implemented based on actual hazy images. The user study indicates that the proposed method can effectively provide color consistency, preserve details, and reduce halo artifacts and noise in the resulting images compared to other state-of-the-art algorithms. When tested on real-world nighttime haze images, our dehazing flow quantitatively achieves 3.06 for NIQE and 2.99 for NR-CDIQA.