A Hybrid Deep Learning Model for Single Image Dehazing

The task of dehazing a single image often proves cumbersome since haze impacts both visibility and image clarity and is complex. For single picture dehazing, this research introduces a new multi-stage deep learning architecture of a GAN, CNN, and attention mechanism. Adversarial training is used to enhance the quality of the dehazing images while the attention mechanism focuses on the hazy areas of the image and the multi-scale feature extraction captures both detail and overall scene. This role integration of the components uses the benefits of one part to offset the disadvantages which characterize the previous solutions. More specifically, the attention models determine where the computations must be used at a higher resolution, the multi-scale CNN extracts features of the image at various scales, and the GAN structure ensures that the synthesized images are not only clear but realistic. The RESIDE dataset is used to train and evaluate the proposed method using quantitative metrics such as the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM). Experimental results show that the suggested strategy outperforms current methodologies in both quantitative and qualitative assessments. From these characteristics, it can be seen that both structural elements and image quality of the assumed dehazed photos of the hybrid model are well restored and realistic, and it has strong adaptability to different kinds of hazy settings.