  This paper introduces a novel approach to image denoising that leverages the
advantages of Generative Adversarial Networks (GANs). Specifically, we propose
a model that combines elements of the Pix2Pix model and the Wasserstein GAN
(WGAN) with Gradient Penalty (WGAN-GP). This hybrid framework seeks to
capitalize on the denoising capabilities of conditional GANs, as demonstrated
in the Pix2Pix model, while mitigating the need for an exhaustive search for
optimal hyperparameters that could potentially ruin the stability of the
learning process. In the proposed method, the GAN's generator is employed to
produce denoised images, harnessing the power of a conditional GAN for noise
reduction. Simultaneously, the implementation of the Lipschitz continuity
constraint during updates, as featured in WGAN-GP, aids in reducing
susceptibility to mode collapse. This innovative design allows the proposed
model to benefit from the strong points of both Pix2Pix and WGAN-GP, generating
superior denoising results while ensuring training stability. Drawing on
previous work on image-to-image translation and GAN stabilization techniques,
the proposed research highlights the potential of GANs as a general-purpose
solution for denoising. The paper details the development and testing of this
model, showcasing its effectiveness through numerical experiments. The dataset
was created by adding synthetic noise to clean images. Numerical results based
on real-world dataset validation underscore the efficacy of this approach in
image-denoising tasks, exhibiting significant enhancements over traditional
techniques. Notably, the proposed model demonstrates strong generalization
capabilities, performing effectively even when trained with synthetic noise.
