Removing Unwanted Noise from Real Scene Images using GANs

Various forms of noise, including as sensor noise, compression artefacts, and ambient disturbances, are frequently present in real-world photographs. These noises can severely reduce the quality of the images and have an effect on future computer vision tasks. In this study, we offer a unique method that uses Generative Adversarial Networks (GANs) to remove undesired sounds from actual scene photos. Since they can produce realistic pictures and understand intricate data distributions, GANs have become a potent tool in the image production and modification domain. Our approach uses GAN model which is made up of a discriminator and a generator network, where the discriminator's job is to discern between actual and created pictures, while the generator's is to produce clean images from noisy inputs. The generator efficiently learns to eliminate noise from input pictures while maintaining important features and structures using an adversarial training procedure. We assess the suggested method using industry-standard benchmark datasets and show encouraging outcomes in terms of picture restoration and noise reduction. By improving picture denoising algorithms, this research advances potential uses in surveillance analysis,
object recognition, and semantic segmentation.