Adaptive Network-Based Pedestrian Detection in Haze Environment

The task of image dehazing, which involves the extraction of atmospheric haze from an image, presents a significant challenge due to the necessity of haze removal without detriment to the inherent quality of the image. The density of haze is subject to considerable variation, and as a result, extant dehazing algorithms frequently struggle to effectively manage differing haze densities. This often culminates in issues such as color distortion, loss of detail, and the emergence of other artifacts. In response to these challenges, this study introduces an innovative adaptive image dehazing algorithm. This algorithm employs a Generative Adversarial Network (GAN) as its foundational structure. Furthermore, an attention mechanism is integrated into the algorithm with the aim of preventing detail loss and concentrating on salient feature information. The proposed algorithm is subjected to rigorous evaluation across diverse datasets. The results demonstrate that it surpasses existing methods in terms of both image quality and robustness in relation to varying haze densities.