As a preprocessing module, denoising can affect the overall image processing; thus, image denoising algorithms are of high significance for image processing and have been studied for several decades. Theoretically, the performances of existing algorithms can be significantly improved, but these improvements are indeed slowing down. To significantly improve the denoising performance, we propose a denoising network method called the fast denoising fusion network (FDFNet). It combines the advantages of a neural network based on block matching and 3D filtering (BM3D-Net) and a fast and flexible denoising convolutional neural network (FFDNet), which simultaneously utilizes internal and external priors to remove noise in a given image; thus, it is a fast and efficient denoising method that delivers superior performance. BM3D-Net and FFDNet can generate two images as basic estimates for fusion. We adopt a combination model to receive the two estimates, which can fuse them effectively to obtain a latent image. Through testing on standard datasets, our experimental results reveal that FDFNet outperformed state-of-the-art denoising methods in terms of both subjective and objective quality. By implementing the entire method on a CNN, the proposed method could exploit the GPU to achieve a higher efficiency. Because the proposed method combines internal and external priors effectively, it could utilize complementary prior knowledge to derive more information.

作为预处理模块，去噪会影响整个图像处理。因此，图像去噪算法对图像处理具有重要意义，并且已经研究了数十年。从理论上讲，可以大大改善现有算法的性能，但是这些改进的确在减缓。为了显着提高去噪性能，我们提出了一种称为快速去噪融合网络（FDFNet）的去噪网络方法。它结合了基于块匹配和3D滤波的神经网络（BM3D-Net）以及快速灵活的去噪卷积神经网络（FFDNet）的优势，该网络同时利用内部和外部先验来去除给定图像中的噪声；因此，这是一种快速高效的去噪方法，可提供出色的性能。BM3D-Net和FFDNet可以生成两个图像作为融合的基本估计。我们采用组合模型来接收两个估计，可以有效地将它们融合以获得潜像。通过对标准数据集进行测试，我们的实验结果表明，在主观和客观质量方面，FDFNet均优于最新的去噪方法。通过在CNN上实现整个方法，所提出的方法可以利用GPU来实现更高的效率。因为所提出的方法有效地结合了内部和外部先验，所以它可以利用互补的先验知识来获得更多的信息。我们的实验结果表明，在主观和客观质量方面，FDFNet均优于最新的去噪方法。通过在CNN上实现整个方法，所提出的方法可以利用GPU来实现更高的效率。由于所提出的方法有效地结合了内部和外部先验，因此可以利用互补的先验知识来获得更多信息。我们的实验结果表明，在主观和客观质量方面，FDFNet均优于最新的去噪方法。通过在CNN上实现整个方法，所提出的方法可以利用GPU来实现更高的效率。由于所提出的方法有效地结合了内部和外部先验，因此可以利用互补的先验知识来获得更多信息。