Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, Guangdong, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, 518055, Guangdong, China.
School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
Neural Netw. 2020 Nov;131:251-275. doi: 10.1016/j.neunet.2020.07.025. Epub 2020 Aug 6.
Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.
深度学习技术在图像去噪领域受到了广泛关注。然而，不同类型的深度学习方法在处理图像去噪方面存在很大差异。具体来说，基于深度学习的判别式学习能够很好地解决高斯噪声问题，基于深度学习的优化模型在估计真实噪声方面非常有效。但是，目前还很少有相关研究来总结不同的深度学习技术用于图像去噪。在本文中，我们对图像去噪中的深度学习技术进行了比较研究。我们首先对用于加性白噪声图像的深度卷积神经网络（CNN）进行分类；用于真实噪声图像的深度 CNN；用于盲去噪的深度 CNN 和用于混合噪声图像的深度 CNN，这代表了噪声、模糊和低分辨率图像的组合。然后，我们分析了不同类型的深度学习方法的动机和原理。接下来，我们在公共去噪数据集上进行了定量和定性分析，比较了最先进的方法。最后，我们指出了一些潜在的挑战和未来研究的方向。
