Dehazing Images Using Vision Transformer Algorithm

Image dehazing is an important task to obtain clear images from blurry vision in low vision individuals. Although traditional methods and deep learning have made progress in this field, there are still challenges, especially in solving the real-life haze problem. This paper studies the application of Vision Transformer architecture, especially the Dehaze Former model, to improve the image dehazing performance. We present a new dataset that provides a comprehensive evaluation of the capabilities of Dehaze Former for difficult haze conditions (and perform function adjustments), and it outperforms existing models using state- of-the-art techniques when measured with new data. We propose several metrics to validate our findings, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Also a good comparison shows the effectiveness of this model in preserving image details and reducing artifacts. This research contributes to the advancement of image dehazing technology by demonstrating the ability of Vision Transformers to process grassy lawns. The insights gained from this study not only highlight the strengths of the Dehaze Former model, but also pave the way for future explorations into dehazing performance using Transformer-based architectures