Celebrity Image Denoising System Using GAN

Abstract

Celebrity pictures are important in public media, entertainment, and branding in the age of social media and digital broadcasting. However, a variety of noises that are introduced during the acquisition, compression, or transmission of these images frequently result in a loss of important facial characteristics and a deterioration in visual quality. While convolutional neural network (CNN)-based approaches and conventional denoising techniques provide partial answers, they usually sacrifice fine textures or structural integrity. A Generative Adversarial Network (GAN)-based method for reliable and high-fidelity denoising of celebrity photos is investigated in this proposal. The suggested model models the underlying distribution of clean celebrity photographs in order to synthesize visually appealing, noise-free photos using the adversarial learning framework.Perceptual loss and structural similarity metrics are integrated into the system to remove complicated noise and maintain identity-specific properties. A high-resolution, detail-preserving denoised image that satisfies the requirements of both functional correctness and aesthetic presen tation in downstream applications including media publishing, facial recognition, and archival restoration is the expected result.