Comparative analysis of dehazing algorithms on real-world hazy images

Images captured in adverse weather conditions (haze, fog, smog, mist, etc.) often suffer significant degradation. Due to the scattering and absorption of these particles, various negative effects, such as reduced visibility, low contrast, and colour distortion are introduced into the image. These degraded images are unsuitable for many computer vision applications, including smart transportation, video surveillance, weather forecasting, and remote sensing. To ensure the reliable operation of such applications, a high-quality haze-free input image is essential, which is supplied by image dehazing techniques. This review categorises recent dehazing methods, highlighting popular approaches within each group. In recent years, deep learning methods and restoration-based techniques using priors have garnered attention, particularly for addressing challenges such as dense and non-homogeneous haze. In this paper, their typical candidates are compared by using real-world hazy images because most data-driven and neural augmentation methods are trained by using synthetic hazy images. Experimental results conducted on real-world hazy images reveal that physics-driven single-image dehazing algorithms exhibit a lack of robustness, while data-driven approaches perform well on thin hazy images but struggle in dense haze conditions. Neural augmentation algorithms, however, effectively combine the strengths of both approaches, offering a better overall solution. By identifying existing gaps in recent methods, this paper provides a valuable resource for both novice and experienced researchers, while pointing towards future directions in this rapidly advancing field.