Abstract
Over the last decade, significant progress has been made in image dehazing, particularly with the advent of deep learning-based methods. However, many of the existing dehazing approaches face critical limitations such as relying on assumptions that fail under complex atmospheric conditions. This results in poor visibility restoration. To address this, this study proposes Dehaze-Attention, an improved image dehazing model designed to handle variable haze densities while preserving essential structural information. The proposed model introduces several contributions. First, it employs advanced feature extraction through convolutional layers to capture foundational details from hazy images. Second, an attention mechanism is integrated into architecture, enabling the model to dynamically focus on relevant features and reduce information loss. Third, a multi-scale network structure is incorporated to process haze across different densities by combining global and local feature analysis. The model was evaluated on a synthesized set of hazy images derived from the UDTIRI dataset under diverse atmospheric conditions. Experimental results demonstrated that the proposed Dehaze-Attention model achieves state-of-the-art performance, with significant improvements in both quantitative metrics (PSNR and SSIM) and subjective evaluations compared to baseline models. The results highlight that the improved model can be used for applications in aerial imaging, autonomous systems, and remote sensing.