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Deep Iterative Down-Up CNN for Image Denoising

机译：用于图像去噪的深度迭代下载CNN

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摘要

Networks using down-scaling and up-scaling of feature maps have been studied extensively in low-level vision research owing to efficient GPU memory usage and their capacity to yield large receptive fields. In this paper, we propose a deep iterative down-up convolutional neural network (DIDN) for image denoising, which repeatedly decreases and increases the resolution of the feature maps. The basic structure of the network is inspired by U-Net which was originally developed for semantic segmentation. We modify the down-scaling and up-scaling layers for image denoising task. Conventional denoising networks are trained to work with a single-level noise, or alternatively use noise information as inputs to address multi-level noise with a single model. Conversely, because the efficient memory usage of our network enables it to handle multiple parameters, it is capable of processing a wide range of noise levels with a single model without requiring noise-information inputs as a work-around. Consequently, our DIDN exhibits state-of-the-art performance using the benchmark dataset and also demonstrates its superiority in the NTIRE 2019 real image denoising challenge.
机译：由于高效的GPU内存使用情况及其产生了大容纳领域的能力，已经在低级视觉研究中进行了利用下缩放和上级特征地图的网络。在本文中，我们提出了一个深刻的迭代下了卷积神经对图像进行去噪，其一再降低，增加了特征地图的分辨率网（没）。网络的基本结构由U-NET启发，其最初开发用于语义细分。我们修改了用于图像去噪任务的下缩放和上缩放图层。传统的去噪网络训练以使用单级噪声，或者可选地使用噪声信息作为输入以解决单个模型的多级噪声。相反，由于我们的网络的有效内存使用使其能够处理多个参数，因此它能够使用单个模型加工各种噪声水平，而无需要求噪声信息输入作为工作。因此，我们的DIDN展示了使用基准数据集的最先进的绩效，并且还在NTIRE 2019真正的图像去噪挑战中展示了其优越性。

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