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，我们提出一种融合Pix2Pix模型与带梯度惩罚的Wasserstein GAN模型的混合架构。该混合框架旨在发挥条件生成对抗网络在图像去噪方面的能力（如Pix2Pix模型所示），同时避免因寻找最优超参数而可能破坏学习过程稳定性的详尽搜索。在所提方法中，生成对抗网络的生成器被用于生成去噪图像，利用条件生成对抗网络实现降噪功能。与此同时，如WGAN-GP所采用的Lipschitz连续性约束在更新过程中的实施，有助于降低模型坍塌的敏感性。这种创新设计使所提模型能够同时受益于Pix2Pix和WGAN-GP的优势，在保证训练稳定性的同时生成更优的去噪结果。基于先前图像到图像转换和生成对抗网络稳定技术的研究，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。基于真实数据集验证的数值结果证实了该方法在图像去噪任务中的效能，较传统技术展现出显著提升。值得注意的是，所提模型展现出强大的泛化能力，即使在合成噪声训练条件下仍能保持有效性能。