• Developed a CycleGAN model for image dehazing, leveraging its ability to learn mappings between unpaired image domains while enforcing cycle consistency and adversarial training. • Explored the impact of data augmentation techniques on improving image quality and generalization. • Implemented the model from scratch without using any pre-trained weights, showcasing a deep understanding of the underlying principles.