Comparative Analysis of Advanced GAN Architectures for Robust Image Dehazing in Synthetic and Real-World Scenarios

One typical phenomenon that impairs or lessens visibility is haze. It creates several issues for applications like security surveillance, autonomous cars, satellite imagery where high resolution captures are necessary. Therefore, clearing up haze from the imagery of scenes is an urgent need for good vision. Different kinds of Generative Adversarial Networks (GAN) are being used in addition to traditional dehazing processes recently to reduce noise and enhance dehazing performance. However, it's unclear how well these algorithms would work with hazy photos taken "in the wild" and how we might assess the state of the art. To close this gap, a thorough comparison of two cutting-edge models— GAN and Wasserstein GAN, that dehaze a single image is presented. Testing was done using a benchmark dataset that included both synthetic and real-world hazy photos. The resulting outcomes are assessed numerically and qualitatively. Among these strategies, the WGAN performs the best.