Image Dehazing using GANs

An developing deep learning technique called picture dehazing, which uses Generative Adversarial Networks (GANs), aims to improve image quality by eliminating haze or fog from photos. Particularly in outdoor settings, haze can drastically reduce an image’s optical clarity and colour fidelity, and conventional dehazing methods frequently fall short of producing the best results without adding artefacts. GANs offer a potent framework for addressing this issue because they are composed of a discriminator and a generator. The discriminator compares the created images’ realism to real, clear photos, while the generator learns to produce clear, haze-free images from their hazy counterparts.

The GAN can capture intricate details and textures in the photos thanks to this adversarial training process, yielding superior results that are more aesthetically pleasing than those produced by traditional techniques. GANs can successfully restore fine details that could be lost in blurry settings because they can simulate the distribution of real images. There are several uses for image dehazing with GANs, such as improving satellite and aerial photography, enhancing photos taken in bad weather, and enabling more engaging visual experiences in computer vision applications. Despite the progress, problems with managing severe weather, maintaining the colours of the original landscape, and guaranteeing computational efficiency still exist. GANs for picture dehazing are poised to transform how humans process and interpret images as research advances.