FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
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FFDNet：为基于 CNN 的图像去噪提供快速灵活的解决方案
DOI：
10.1109/tip.2018.2839891
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发表时间：
2018-09-01
影响因子：
10.6
通讯作者：
Zhang, Lei
中科院分区：
文献类型：
--
作者：
Zhang, Kai;Zuo, Wangmeng;Zhang, Lei
鉴别学习方法由于其推理速度快、性能好等优点，在图像去噪中得到了广泛的研究。然而，这些方法大多针对每个噪声水平学习特定的模型，并且需要多个模型来对具有不同噪声水平的图像进行去噪。同时，该算法在处理空间变异噪声方面缺乏灵活性，限制了其在实际去噪中的应用。为了解决这些问题，我们提出了一种快速灵活的去噪卷积神经网络，即FFDNet，其输入是可调的噪声水平图。所提出的FFDNet适用于下采样的子图像，在推理速度和去噪性能之间实现了良好的权衡。与现有的判别去噪器相比，FFDNet具有几个理想的特性，包括：1）处理宽范围噪声水平的能力（即，[0，75]）有效地使用单个网络; 2）通过指定非均匀噪声水平图来去除空间变化噪声的能力;以及3）即使在CPU上也比基准BM 3D更快的速度，而不会牺牲去噪性能。对合成和真实的噪声图像进行了广泛的实验，以评估FFDNet与最先进的去噪器的比较。结果表明，FFDNet是有效的和高效的，使其非常有吸引力的实际去噪应用。
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including: 1) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network; 2) the ability to remove spatially variant noise by specifying a non-uniform noise level map; and 3) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.