Quantum Generative Adversarial Networks For Image De-Noising

Quantum Generative Adversarial Networks (QGANs) have emerged as a promising avenue for image de-noising, leveraging the principles of quantum computing to address challenges in traditional methodologies. In this study, we introduce a novel approach for image de-noising with QGANs and evaluate its performance against conventional techniques. We preprocess images into quantum states and train a QGAN model to generate high-quality de-noised images. Our methodology involves implementing QGAN architecture, quantum preprocessing techniques, and evaluation metrics of the model. Through extensive experimentation and comparison with classical methods, we introduce the performance of our QGAN-based approach in reducing noise and enhancing image quality. This research contributes to the advancement of quantum computing applications in image processing and lays the foundation for future developments in quantum image d-enoising.