Abstract
Abstract: This paper presents an AI-ML-based defogging and smoke removal algorithm that uses a hybrid model combined with a convolutional neural network (CNN) and Transformer. The proposed model uses the U-Net backbone to extract local detailed information, uses the FFA-Net maintenance mechanism to update regions with different burning conditions, and uses the Dehamer transformer encoder to capture and manage the comprehensive information of remote locations in the image. This combination works well in a wide range of climate and smoke conditions, producing high-quality images with accurate colors. The model is evaluated on synthetic and real data, yielding results that outperform today's traditional methods and deep learning models. Its effective capabilities make it suitable for immediate use, making it possible to eliminate and eliminate smoke technology in difficult situations.
Published Version
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