Efficient Fog Removal Technique For Video Processing

Foggy weather conditions frequently reduce visibility and degrade video stream quality, affecting a variety of real-world applications such as surveillance, autonomous navigation, and outdoor video recording. This paper proposes an efficient method for defogging video sequences, which will improve scene visibility and visual clarity. The proposed technique successfully removes atmospheric haze from subsequent frames in real-time by combining temporal consistency and image de-hazing algorithms. The incorporation of the dark channel prior, which accurately measures the amount of haze or fog in each frame, is a key component of the method. By leveraging the inherent statistical properties of haze-free regions, the dark channel prior enables precise transmission map estimation, facilitating efficient haze removal. Additionally, motion compensation and fusion techniques are employed to maintain temporal coherence and guarantee consistent fog removal across frames. To evaluate the efficacy of the suggested technique, a number of experiment scenarios were conducted on a variety of foggy video sequences captured in a variety of weather conditions. The results demonstrate that the suggested method successfully removes haze while keeping important image details and colors, leading to increased visual clarity. Analysis in comparison to state-of-the-art fog removal techniques shows the approach’s superior real time performance and quality. Visibility in foggy video sequences is significantly improved by the suggested efficient fog removal technique that uses video processing. Because it is suitable for applications that require immediate fog clearance, like live video surveillance and autonomous vehicles.