Khan Aamir, Jin Weidong, Haider Amir, Rahman MuhibUr, Wang Desheng
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China.
China-ASEAN International Joint Laboratory of Integrated Transport, Nanning University, Nanning 530000, China.
Sensors (Basel). 2021 Apr 24;21(9):2998. doi: 10.3390/s21092998.
Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.
图像去噪是一项具有挑战性的任务，在众多计算机视觉和图像处理问题中至关重要。本研究提出并将基于生成对抗网络的图像去噪训练架构应用于多级高斯图像去噪任务。基于卷积神经网络的去噪方法存在模糊问题，会使去噪后的图像在纹理细节上变得模糊。为了解决模糊问题，我们首先对该问题的成因进行了理论研究。随后，我们提出了一种对抗性高斯去噪器网络，它将基于生成对抗网络的对抗学习过程用于图像去噪任务。该框架通过鼓励去噪器网络找到清晰无噪声图像的分布而非模糊图像的分布来解决模糊问题。实验结果表明，所提出的框架能够有效解决模糊问题，并且比当前最先进的去噪方法具有更高的去噪效率。
