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.

翻译：本文提出一种利用生成对抗网络优势的图像去噪新方法。具体而言，我们构建了一个融合Pix2Pix模型与带梯度惩罚的Wasserstein GAN（WGAN-GP）要素的混合模型。该框架旨在充分发挥条件生成对抗网络在Pix2Pix模型中展现的去噪能力，同时避免因过度搜索超参数而破坏学习过程稳定性的问题。在所提方法中，生成器通过条件生成对抗网络实现噪声抑制以生成去噪图像；同时，WGAN-GP中采用的Lipschitz连续性约束在参数更新时有效降低了模式崩溃的敏感性。这种创新设计使模型能兼顾Pix2Pix与WGAN-GP的双重优势，在保证训练稳定性的同时获得更优的去噪效果。基于图像翻译与生成对抗网络稳定化技术的先前研究，本工作凸显了生成对抗网络作为通用去噪解决方案的潜力。论文详细阐述了该模型的开发与测试过程，并通过数值实验验证其有效性。数据集通过对干净图像添加合成噪声构建而成。基于真实数据集的验证结果表明，该方法在图像去噪任务中显著优于传统技术，尤其展现出强大的泛化能力——即使在合成噪声数据上训练后仍能保持优异性能。