With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they are computationally demanding, which makes their deployment especially difficult for mobile terminals. Second, experimental evidence shows that CNNs often over-smooth regular textures present in images, in contrast to traditional non-local models. In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN. This solution gives full latitude to the advantages of both models. We apply this framework to two GPU implementations of classic nonlocal algorithms (NLM and BM3D) and observe a substantial gain in both cases, performing better than the state-of-the-art with low computational requirements. Our solution is between 10 and 20 times faster than CNNs with equivalent performance and attains higher PSNR. In addition the final method shows a notable gain on images containing complex textures like the ones of the MIT Moire dataset.

翻译：随着卷积神经网络（CNN）的广泛应用，传统基于模型的去噪算法已被超越。然而，CNN面临两个问题：其一，计算需求高，尤其难以部署于移动终端；其二，实验证据表明，与传统非局部模型相比，CNN常过度平滑图像中的规则纹理。本文提出一种结合非局部算法与轻量级残差CNN的解决方案，充分发挥两种模型的优势。我们将该框架应用于两种经典非局部算法（NLM和BM3D）的GPU实现，并在两种情形下均观察到显著性能提升，以较低计算需求超越现有最优方法。与性能相当的CNN相比，本方案速度快10至20倍，且获得更高的峰值信噪比（PSNR）。此外，最终方法在包含复杂纹理的图像（如MIT莫尔条纹数据集）上展现出显著增益。